Obesity paradox in associations between body mass index and diabetes-related hospitalization and mortality in patients with type 2 diabetes: Retrospective cohort studies

AimDiabetes is a primary cause of hospitalization and in-hospital mortality. However, studies exploring the relationships between body mass index (BMI) and hospitalization-related and mortality-related outcomes in patients with type 2 diabetes are lacking.MethodsOur data were obtained from two independent retrospective cohort studies, namely, the Taiwan Diabetes Study (Taiwan DS), providing hospitalization outcome measures, and the Taichung Diabetes Study (Taichung DS) that can be linked with the National Death Registry dataset. BMI and hospitalization, in-hospital mortality, and all-cause and cause-specific death events were analyzed by Cox proportional hazard regression model.ResultsA total of 3,541, 38,779, and 10,399 patients died during hospitalization, hospitalized for all-cause and diabetes-related events, respectively, in the Taiwan DS cohort. Moreover, 685 deaths were identified in the Taichung DS cohort. Compared with patients with increasing-but-acceptable-risk obesity, multivariable-adjusted hazard ratios (HRs) of in-hospital mortality, all-cause hospitalization, hospitalization due to diabetes, hypoglycaemia, and renal failure for patients who were underweight were 2.09 (95% confidence interval 1.73, 2.51), 1.39 (1.28, 1.50), 1.69 (1.49, 1.90), 1.87 (1.34, 2.61), and 1.55 (1.26, 1.91). Adjusted HRs of all-cause mortality and non-expanded CVD-related mortality in patients with underweight were 2.02 (1.28, 3.21), and 2.27 (1.28, 4.03).ConclusionsThe BMI associated with the best survival and less hospitalization was the higher-high-risk obesity (≥ 27.5 kg/m²) category. We observed obesity paradox for mortality outcomes, which should be addressed by further research, particularly on whether randomized controlled trials of adopting a healthy lifestyle for patients with obesity can improve type 2 diabetes patients’ survival.     

Machine-Learning-Based In-Hospital Mortality Prediction for Transcatheter Mitral Valve Repair in the United States

BackgroundTranscatheter mitral valve repair (TMVR) utilization has increased significantly in the United States over the last years. Yet, a risk-prediction tool for adverse events has not been developed. We aimed to generate a machine-learning-based algorithm to predict in-hospital mortality after TMVR.MethodsPatients who underwent TMVR from 2012 through 2015 were identified using the National Inpatient Sample database. The study population was randomly divided into a training set (n = 636) and a testing set (n = 213). Prediction models for in-hospital mortality were obtained using five supervised machine-learning classifiers.ResultsA total of 849 TMVRs were analyzed in our study. The overall in-hospital mortality was 3.1%. A naïve Bayes (NB) model had the best discrimination for fifteen variables, with an area under the receiver-operating curve (AUC) of 0.83 (95% CI, 0.80–0.87), compared to 0.77 for logistic regression (95% CI, 0.58–0.95), 0.73 for an artificial neural network (95% CI, 0.55–0.91), and 0.67 for both a random forest and a support-vector machine (95% CI, 0.47–0.87). History of coronary artery disease, of chronic kidney disease, and smoking were the three most significant predictors of in-hospital mortality.ConclusionsWe developed a robust machine-learning-derived model to predict in-hospital mortality in patients undergoing TMVR. This model is promising for decision-making and deserves further clinical validation.     

Excess diabetes-related deaths: The role of comorbidities through different phases of the COVID-19 pandemic

Background and aimsDiabetes confers an excess risk of death to COVID-19 patients. Causes of death are now available for different phases of the pandemic, encompassing different viral variants and COVID-19 vaccination. The aims of the present study were to update multiple causes of death data on diabetes-related mortality during the pandemic and to estimate the impact of common diabetic comorbidities on excess mortality.Methods and resultsDiabetes-related deaths in 2020–2021 were compared with the 2018–2019 average; furthermore, age-standardized rates observed during the pandemic were compared with expected figures obtained from the 2008–2019 time series through generalized estimating equation models. Changes in diabetes mortality associated with specific comorbidities were also computed. Excess diabetes-related mortality was +26% in 2020 and +18% in 2021, after the initiation of the vaccination campaign. The presence of diabetes and hypertensive diseases was associated with the highest mortality increase, especially in subjects aged 40–79 years, +41% in 2020 and +30% in 2021.ConclusionThe increase in diabetes-related deaths exceeded that observed for all-cause mortality, and the risk was higher when diabetes was associated with hypertensive diseases. Notably, the excess mortality decreased in 2021, after the implementation of vaccination against COVID-19.     

Prediction of Survival in Asian Patients Hospitalized With Heart Failure: Validation of the OPTIMIZE-HF Risk Score

BackgroundRisk scores predicting in-patient mortality in heart failure patients have not been designed specifically for Asian patients. We aimed to validate and recalibrate the OPTIMIZE-HF risk model for in-hospital mortality in a multiethnic Asian population hospitalized for heart failure.Methods and ResultsData from the Singapore Cardiac Databank Heart Failure on patients admitted for heart failure from January 1, 2008, to December 31, 2013, were included. The primary outcome studied was in-hospital mortality. Two models were compared: the original OPTIMIZE-HF risk model and a modified OPTIMIZE-HF risk model (similar variables but with coefficients derived from our cohort). A total of 15,219 patients were included. The overall in-hospital mortality was 1.88% (n = 286). The original model had a C-statistic of 0.739 (95% CI 0.708–0.770) with a good match between predicted and observed mortality rates (Hosmer-Lemeshow statistic 13.8; P = .086). The modified model had a C-statistic of 0.741 (95% CI 0.709–0.773) but a significant difference between predicted and observed mortality rates (Hosmer-Lemeshow statistic 17.2; P = .029). The modified model tended to underestimate risk at the extremes (lowest and highest ends) of risk.ConclusionsWe provide the first independent validation of the OPTIMIZE-HF risk score in an Asian population. This risk model has been shown to perform reliably in our Asian cohort and will potentially provide clinicians with a useful tool to identify high-risk heart failure patients for more intensive management.     

Can the in-hospital mortality gap between STEMI patients with and without diabetes mellitus be reduced? The cardio-renal hypothesis

Background and aimsDiabetes mellitus (DM) is a frequent comorbidity in ST-elevation-myocardial infarction (STEMI) patients and carries a higher risk of in-hospital mortality. We recently demonstrated that the higher in-hospital mortality of STEMI patients with DM, when compared to that of patients without DM, is mainly associated with their more frequent cardiac and renal dysfunction. These exploratory results prompted us to hypothesize that this higher risk in DM patients is mediated by their lower cardio-renal functional reserve.Methods and resultsWe included 5152 STEMI patients treated with primary angioplasty. By using an advanced statistical methodology (path analysis), able to clarify the putative causal paths between variables of interest, we reported that the higher in-hospital mortality of STEMI patients with DM is possibly caused by its adverse impact on cardio-renal function.ConclusionThis statistical approach allows to reinforce the well-known notion that DM is associated with an increased in-hospital mortality risk in STEMI and sheds lights on the causal relationship among DM, cardio-renal dysfunction, and higher in-hospital mortality. Whether the mortality gap between DM and non-DM patients with STEMI can be reduced by pharmacological strategies combining cardio-renal protective effects is an intriguing question that deserves an answer in the future.     

Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement

ObjectivesThis study sought to develop and compare an array of machine learning methods to predict in-hospital mortality after transcatheter aortic valve replacement (TAVR) in the United States.BackgroundExisting risk prediction tools for in-hospital complications in patients undergoing TAVR have been designed using statistical modeling approaches and have certain limitations.MethodsPatient data were obtained from the National Inpatient Sample database from 2012 to 2015. The data were randomly divided into a development cohort (n = 7,615) and a validation cohort (n = 3,268). Logistic regression, artificial neural network, naive Bayes, and random forest machine learning algorithms were applied to obtain in-hospital mortality prediction models.ResultsA total of 10,883 TAVRs were analyzed in our study. The overall in-hospital mortality was 3.6%. Overall, prediction models’ performance measured by area under the curve were good (>0.80). The best model was obtained by logistic regression (area under the curve: 0.92; 95% confidence interval: 0.89 to 0.95). Most obtained models plateaued after introducing 10 variables. Acute kidney injury was the main predictor of in-hospital mortality ranked with the highest mean importance in all the models. The National Inpatient Sample TAVR score showed the best discrimination among available TAVR prediction scores.ConclusionsMachine learning methods can generate robust models to predict in-hospital mortality for TAVR. The National Inpatient Sample TAVR score should be considered for prognosis and shared decision making in TAVR patients.     

The Association Between Socioeconomic Status,Sex, Race / Ethnicity and In-Hospital Mortality Among Patients Hospitalized for Heart Failure

BackgroundThe association between socioeconomic status (SES), sex, race / ethnicity and outcomes during hospitalization for heart failure (HF) has not previously been investigated.Methods and ResultsWe analyzed HF hospitalizations in the United States National Inpatient Sample between 2015 and 2017. Using a hierarchical, multivariable Poisson regression model to adjust for hospital- and patient-level factors, we assessed the association between SES, sex, and race / ethnicity and all-cause in-hospital mortality. We estimated the direct costs (USD) across SES groups. Among 4,287,478 HF hospitalizations, 40.8% were in high SES, 48.7% in female, and 70.0% in White patients. Relative to these comparators, low SES (homelessness or lowest quartile of median neighborhood income) (relative risk [RR] 1.02, 95% confidence interval [CI] 1.00–1.05) and male sex (RR 1.09, 95% CI 1.07–1.11) were associated with increased risk, whereas Black (RR 0.79, 95% CI 0.76–0.81) and Hispanic (RR 0.90, 95% CI 0.86–0.93) race / ethnicity were associated with a decreased risk of in-hospital mortality (5.1% of all hospitalizations). There were significant interactions between race / ethnicity and both, SES (P < .01) and sex (P = .04), such that racial/ethnic differences in outcome were more pronounced in low SES groups and in male patients. The median direct cost of admission was lower in low vs high SES groups ($9324.60 vs $10,940.40), female vs male patients ($9866.60 vs $10,217.10), and Black vs White patients ($9077.20 vs $10,019.80). The median costs increased with SES in all demographic groups primarily related to greater procedural utilization.ConclusionsSES, sex, and race / ethnicity were independently associated with in-hospital mortality during HF hospitalization, highlighting possible care disparities. Racial/ethnic differences in outcome were more pronounced in low SES groups and in male patients.     

National Patterns of Heart Failure Hospitalizations and Mortality by Sex and Age

BackgroundEarlier work has demonstrated significant sex and age disparities in ischemic heart disease. However, it remains unclear if an age or sex gap exists for heart failure (HF) patients.Methods and ResultsUsing data from the 2007–2008 Healthcare Cost and Utilization Project, we constructed hierarchic regression models to examine sex differences and age-sex interactions in HF hospitalizations and in-hospital mortality. Among 430,665 HF discharges, 51% were women and 0.3%, 27%, and 73% were aged <25, 25–64, and >64 years respectively. There were significant sex differences among HF risk factors, with a higher prevalence of coronary disease among men. Men had higher hospitalization rates for HF and in-hospital mortality across virtually all ages. The relationship between age and HF mortality appeared U-shaped; mortality rates for ages <25, 25–64, and >64 years were 2.9%, 1.4%, and 3.8%, respectively. No age-sex interaction was found for in-hospital mortality for adults >25 years old.ConclusionsUsing a large nationally representative administrative dataset we found age and sex disparities in HF outcomes. In general, men fared worse than women regardless of age. Furthermore, we found a U-shaped relationship between age and in-hospital mortality during an HF hospitalization, such that young adults have similar mortality rates to older adults. Additional studies are warranted to elucidate the patient-specific and treatment characteristics that result in these patterns.     

Is it possible to simplify risk stratification scores for patients with ST-segment elevation myocardial infarction undergoing primary angioplasty?

IntroductionThere are several risk scores for stratification of patients with ST-segment elevation myocardial infarction (STEMI), the most widely used of which are the TIMI and GRACE scores. However, these are complex and require several variables. The aim of this study was to obtain a reduced model with fewer variables and similar predictive and discriminative ability.MethodsWe studied 607 patients (age 62 years, SD=13; 76% male) who were admitted with STEMI and underwent successful primary angioplasty. Our endpoints were all-cause in-hospital and 30-day mortality. Considering all variables from the TIMI and GRACE risk scores, multivariate logistic regression models were fitted to the data to identify the variables that best predicted death.ResultsCompared to the TIMI score, the GRACE score had better predictive and discriminative performance for in-hospital mortality, with similar results for 30-day mortality. After data modeling, the variables with highest predictive ability were age, serum creatinine, heart failure and the occurrence of cardiac arrest. The new predictive model was compared with the GRACE risk score, after internal validation using 10-fold cross validation. A similar discriminative performance was obtained and some improvement was achieved in estimates of probabilities of death (increased for patients who died and decreased for those who did not).ConclusionIt is possible to simplify risk stratification scores for STEMI and primary angioplasty using only four variables (age, serum creatinine, heart failure and cardiac arrest). This simplified model maintained a good predictive and discriminative performance for short-term mortality.     

The prognostic factors of in-hospital death among patients with pneumonic COPD acute exacerbation

BackgroundPneumonic acute exacerbation of chronic obstructive pulmonary disease (COPD-AE) is associated with worse outcomes compared with non-pneumonic COPD-AE. We aimed to explore prognostic factors among patients with pneumonic COPD-AE.MethodsThis multicentered retrospective cohort study was conducted across five hospitals in Japan. Hospitalized patients ≥40 years of age with pneumonic COPD-AE who were administered systemic corticosteroids during hospitalization were included. Patients with other causes of respiratory failure, daily systemic steroid users, and patients who were not treated with systemic steroids were excluded. Based on existing clinical prediction models, the following potential prognostic factors were selected in advance: age, blood eosinophil count, blood urea nitrogen, respiratory rate, diastolic blood pressure, and altered mental status. Multivariate logistic regression was conducted to determine the association between potential prognostic factors and in-hospital death.ResultsAfter excluding 897 patients based on the exclusion criteria, 669 patients with pneumonic COPD-AE who were administered systemic corticosteroids were included. The in-hospital mortality rate was 5.1%. Altered mental status was associated with mortality (odds ratio, 4.47; 95% confidence intervals, 2.00 to 10.00), and eosinophilia was associated with a lower risk of mortality (odds ratio, 0.19; 95% confidence intervals: 0.06 to 0.56).ConclusionsAltered mental status may be a prognostic factor for in-hospital death among patients with pneumonic COPD-AE who were administered systemic corticosteroids. Moreover, eosinophilia may be a prognostic factor for lower in-hospital mortality rate among these patients.     

Clinical Impact of Atrial Fibrillation on Short-Term Outcomes and In-Hospital Mortality in Patients with Takotsubo Syndrome: A Propensity-Matched National Study

IntroductionTakotsubo Syndrome (TS) patients are at high risk of developing atrial fibrillation. We sought to investigate the outcomes and economic impact of atrial fibrillation on TS patients utilizing the National Inpatient Sample.MethodsPatients with TS were identified in the National Inpatient Sample (NIS) database between 2010 and 2014 using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), and subsequently were divided into two groups, those with and without atrial fibrillation. The primary outcome was all-cause in-hospital mortality in the two groups. Secondary outcomes were in-hospital complications. We also evaluated the length of hospital stay and the cost of hospitalization. Propensity score-matched analysis was performed to address potential confounding factors.ResultsAmong the study population, the prevalence of atrial fibrillation was 17.57%. After matching, the atrial fibrillation group had no significant increase of in-hospital mortality (OR: 1.13; 95% CI: 0.94–1.35, p = 0.211). However, atrial fibrillation patients were more likely to develop cardiac arrest and ventricular arrhythmias (OR: 1.51, 95% CI: 1.26–1.80, p < 0.0001), have higher rate of major cardiac complications when combined as a single endpoint in-hospital complication (OR: 1.16, 95% CI: 1.04–1.29, p: 0.006), also they were more likely to stay longer in hospital (OR: 1.13, 95% CI: 1.08–1.19, p < 0.0001), and have increased cost of hospitalization (OR: 1.13, 95% CI 1.07–1.20, p < 0.0001).ConclusionAtrial fibrillation does not increase in-hospital mortality in patients presenting with TS. However atrial fibrillation is associated with an increased risk of ventricular arrhythmias, length of stay, non-routine discharges and cost of hospitalization.     

Development and Validation of an Acute Heart Failure-Specific Mortality Predictive Model Based on Administrative Data

Background

Acute heart failure (AHF) with its high in-hospital mortality is an increasing burden on healthcare systems worldwide, and comparing hospital performance is required for improving hospital management efficiency. However, it is difficult to distinguish patient severity from individual hospital care effects. The aim of this study was to develop a risk adjustment model to predict in-hospital mortality for AHF using routinely available administrative data.

Methods

Administrative data were extracted from 86 acute care hospitals in Japan. We identified 8620 hospitalized patients with AHF from April 2010 to March 2011. Multivariable logistic regression analyses were conducted to analyze various patient factors that might affect mortality. Two predictive models (models 1 and 2; without and with New York Heart Association functional class, respectively) were developed and bootstrapping was used for internal validation. Expected mortality rates were then calculated for each hospital by applying model 2.

Results

The overall in-hospital mortality rate was 7.1%. Factors independently associated with higher in-hospital mortality included advanced age, New York Heart Association class, and severe respiratory failure. In contrast, comorbid hypertension, ischemic heart disease, and atrial fibrillation/flutter were found to be associated with lower in-hospital mortality. Both model 1 and model 2 demonstrated good discrimination with c-statistics of 0.76 (95% confidence interval, 0.74-0.78) and 0.80 (95% confidence interval, 0.78-0.82), respectively, and good calibration after bootstrap correction, with better results in model 2.

Conclusions

Factors identifiable from administrative data were able to accurately predict in-hospital mortality. Application of our model might facilitate risk adjustment for AHF and can contribute to hospital evaluations.     

Impact and Outcomes of Patients with Congestive Heart Failure Complicating Non-ST-Segment Elevation Myocardial Infarction,Results from a Nationally-Representative United States Cohort

IntroductionCongestive heart failure (CHF) is seen in up to 13–25% of patients with NSTEMI. Recent data describing the impact of congestive heart failure (CHF) on in-hospital outcomes in patients with non-ST-segment elevation myocardial infarction (NSTEMI) in the United States is limited. We sought to examine the in-hospital outcomes, and management of CHF in patients admitted to the hospital with NSTEMI.MethodsNational Inpatient Sample (NIS) database (2010–2014) was analyzed to identify patients with NSTEMI using ICD-9-CM codes. The primary outcome was in-hospital mortality. Propensity score-matching analysis compared mortality in CHF patients to matched controls without CHF.ResultsOf 247,624 patients with NSTEMI, 84,115 (34%) had CHF. Patients with CHF were less likely to receive percutaneous coronary intervention (PCI) [20.48% vs. 40.9%, P < 0.001] or coronary artery bypass grafting (CABG) [8.2% vs 9.6%, P < 0.001] during hospitalization. Also, they had longer lengths of stay and higher risk for in-hospital adverse outcomes. CHF was the strongest predictor of in-hospital death. The increased mortality risk was persistent after propensity matching (RR 1.27; 95% CI 1.22 to 1.33).ConclusionCHF among patients with NSTEMI is associated with increased risk for in-hospital mortality and adverse outcomes.     

Incidence,characteristic and outcomes of ventilator-associated pneumonia among type 2 diabetes patients: An observational population-based study in Spain

BackgroundTo describe incidence, characteristics and outcomes of ventilator-associated pneumonia (VAP) during hospitalization among patients with or without type 2 diabetes (T2DM).MethodsWe used the Spanish national hospital discharge database to select all hospitalization with VAP in subjects aged 40 years or more from 2010 to 2014. We analyzed incidence, patient comorbidities, procedures, pneumonia pathogens and in-hospital outcomes according to diabetes status (T2DM and no-diabetes). We used propensity score analysis to estimate the effect of T2DM on in-hospital mortalityResultsIn 7952 admissions, the patient developed a VAP (13.6% with T2DM). Adjusted incidence rate of VAP was slightly, but significantly, higher in T2DM than in non-diabetic patients (36.46[95% CI 34.41–38.51] vs. 32.57[95% CI 31.40–33.74] cases per 100,000/inhabitants). T2DM people were older and had higher Charlson comorbidity index than non-diabetic people. T2DM patients had a lower mean number of failing organs than non-diabetic patients (1.20 SD 1.17 vs. 1.45 SD 1.44, p < 0.001). Pseudomonas was the most frequently isolated agent in both groups. IHM was 41.92% for T2DM patients and 37.91% for non-diabetic patients (p < 0.05). Factors associated with a higher mortality in both groups included: older age, more comorbidities and primary diagnoses of vein or artery occlusion, pulmonary disease and cancer. T2DM was not associated with a higher in-hospital mortality after adjustment using a propensity score (OR 0.88; 95% CI 0.76–1.35).ConclusionsVAP incidence rates were higher among T2DM patients. In-hospital mortality was higher among the older patients and those with more co-morbid conditions. T2DM does not predict higher mortality in VAP during hospitalization.     

Systemic lupus erythematosus is associated with poor outcome after acute myocardial infarction

BackgroundSystemic lupus erythematosus (SLE) is associated with a higher risk of cardiovascular disease. However, it is not clear whether or not SLE is associated with poor outcomes after acute myocardial infarction (AMI).Methods and resultsUsing the Taiwan National Health Insurance Database, we identified the SLE group as patients with AMI who have a concurrent discharge diagnosis of SLE. We also selected an age-, sex-, hospital level-, and admission calendar year-matched non-SLE group at a ratio of 1:3 from the total non-SLE group. One hundred fifty-one patients with SLE, 113,791 patients without SLE, and 453 matched patients without SLE were admitted with a diagnosis of AMI. Patients with SLE were significantly younger, predominantly female, and more likely to have chronic kidney disease than those without SLE. The in-hospital mortality rates were 12.6%, 9.0%, and 4.2% in the SLE, total non-SLE, and matched non-SLE groups, respectively. The in-hospital mortality was significantly higher in the SLE group than in the total non-SLE group (OR = 1.98; 95% CI = 1.2–3.26) and the matched non-SLE group (mortality OR = 2.20; 95% CI = 1.06–4.58). In addition, the SLE group was associated with a borderline significant risk of prolonged hospitalization when compared with the non-SLE group.ConclusionSLE is associated with a higher risk of in-hospital mortality and a borderline significantly higher risk of prolonged hospitalization after AMI.     

Comparative Analysis of Established Risk Scores and Novel Hemodynamic Metrics in Predicting Right Ventricular Failure in Left Ventricular Assist Device Patients

BackgroundRight ventricular failure (RVF) portends poor outcomes after left ventricular assist device (LVAD) implantation. Although numerous RVF predictive models have been developed, there are few independent comparative analyses of these risk models.Methods and ResultsRVF was defined as use of inotropes for >14 days, inhaled pulmonary vasodilators for >48 hours or unplanned right ventricular mechanical support postoperatively during the index hospitalization. Risk models were evaluated for the primary outcome of RVF by means of logistic regression and receiver operating characteristic curves. Among 93 LVAD patients with complete data from 2011 to 2016, the Michigan RVF score (C = 0.74 [95% CI 0.61–0.87]; P = .0004) was the only risk model to demonstrate significant discrimination for RVF, compared with newer risk scores (Utah, Pitt, EuroMACS). Among individual hemodynamic/echocardiographic metrics, preoperative right ventricular dysfunction (C = 0.72 [95% CI 0.58–0.85]; P = .0022) also demonstrated significant discrimination of RVF. The Michigan RVF score was also the best predictor of in-hospital mortality (C = 0.67 [95% CI 0.52–0.83]; P = .0319) and 3-year survival (Kaplan-Meier log-rank 0.0135).ConclusionsIn external validation analysis, the more established Michigan RVF score—which emphasizes preoperative hemodynamic instability and target end-organ dysfunction—performed best, albeit modestly, in predicting RVF and demonstrated association with in-hospital and long-term mortality.     

Real-time mortality risk calculator following pancreatoduodenectomy: quantifying the impact of perioperative events

BackgroundEstimation of mortality risk traditionally has only included preoperative factors. We sought to develop “real-time” mortality risk-calculator for patients who undergo pancreatoduodenectomy (PD) based on preoperative factors, as well as events that occurred during the course of patient's surgery and hospitalization.MethodsPatients who underwent PD from 2014 to 2018 were identified in the ACS-NSQIP dataset. Training and validation cohorts were created. Pre-, intra-, and post-operative models to predict 30-day mortality were developed based on perioperative variables selected by stepwise cox regression analyses; model performance was assessed using AUC.ResultsAmong 17,683 patients who underwent PD, 1.6% died within 30-days. Patient factors and events associated with 30-day mortality were incorporated into a risk calculator (https://ktsahara.shinyapps.io/Real-timePD/). The accuracy of the risk-calculator increased relative to hospital time-course in both the training (AUC, pre-:0.696, intra-:0.724, post-operative:0.871) and validation (AUC, pre-:0.681, intra-:0.702, post-operative:0.850) cohorts. One in 3 patients had a concordant calculated risk of mortality using pre-versus postoperative variables to inform the risk model (kappa = 0.474).ConclusionRisk of mortality fluctuated over the hospital course following PD and preoperative risk assessment was often discordant with risk assessed at other periods. The proposed “real-time” calculator may help better stratify patients with increased risk of 30-day mortality.     

Contemporary Trends of Clinical Outcomes in Primary Left Ventricular Assist Device Implantation and Postprocedure High-Risk Categories

ObjectiveWe aimed to analyze trends of 30-day readmission and find high-risk patients associated with increased risk of mortality, resource use, and readmission after primary left ventricular assist device (LVAD) implantation. Limited data exist on the contemporary trends of readmission rates and patients at a higher risk of worse outcomes after LVAD implantation.Methods and ResultsThis is a retrospective study of adults from the Nationwide Readmission Database who underwent primary durable LVAD implantation from 2010 to 2018. The main outcomes were 30-day readmission rates and their trends in patients with primary durable LVAD implantation from 2010 to 2018. This study also sought to identify patients at the highest risk for readmission, in-hospital mortality, and resource use. A total of 31,002 adults with primary durable LVAD implantation were included in the present analysis. Overall, 3808 patients (12.3%) died and 27,168 (87.6%) were discharged alive. Of those discharged alive, 8303 patients (30.6%) were readmitted within 30 days. The trend of 30-day all-cause readmission among LVAD implantation patients remained similar from 2010 to 2018 (P = .809). The in-hospital mortality rate during the index hospitalization decreased significantly (P = .014), and the mean cost of an index hospitalization increased (P = .031) during the study period. The patients with post-LVAD in-hospital cardiac, vascular, and thromboembolic complications (ie, high-risk patients) had the highest mortality, resource use, and readmission rates compared with patients without major complications.ConclusionsThis study found that the readmission rates associated with LVAD implantation did not change from 2010 to 2018 and identified high-risk patients who may benefit from closer monitoring after primary LVAD implantation.     

Serum creatinine as a predictor of mortality in patients readmitted to the intensive care unit after cardiac surgery: a retrospective cohort study in China

BackgroundPatients readmitted to the intensive care unit (ICU) after cardiac surgery have a high mortality rate. The relationship between renal function and in-hospital mortality in readmitted patients has not been well demonstrated.MethodsWe retrospectively evaluated cardiac surgery patients who were readmitted to the ICU at least once. Data on serum creatinine levels before surgery and on the day of ICU readmission were collected. The estimated glomerular filtration rate (eGFR) was calculated according to the creatinine-based Chronic Kidney Disease-Epidemiology Collaboration equation. We used logistic regression models and restricted cubic spline curves with four knots (5%, 35%, 65%, 95%) to investigate the relationship between renal function indicators and mortality.ResultsOf the 184 patients evaluated, 30 patients died during hospitalization, yielding a mortality rate of 16.30%. Cardiac dysfunction (n=84, 45.65%) and respiration disorder (n=51, 27.72%) were the most common reasons for ICU readmission. Creatinine [odds ratio (OR): 1.14, 95% confidence interval (CI): 1.07–1.25] and eGFR (OR: 0.95, 95% CI: 0.93–0.98) were independently associated with in-hospital mortality after adjusting for various confounders. Both creatinine level and eGFR had a linear association with in-hospital mortality (P for non-linearity ˃0.05).ConclusionRenal function is significantly associated with the in-hospital mortality of patients readmitted to the ICU after cardiac surgery, as evidenced by the independent correlation of both creatinine and eGFR with in-hospital mortality.     

In-hospital mortality following acute exacerbations of chronic obstructive pulmonary disease

BACKGROUND: Acute exacerbations of chronic obstructive pulmonary disease (COPD) are a frequent cause of hospitalization in the United States. Previous studies of selected populations of patients with COPD have estimated in-hospital mortality to range from 4% to 30%. Our objective was to obtain a generalizable estimate of in-hospital mortality from acute exacerbation of COPD in the United States and to identify predictors of in-hospital mortality using administrative data. METHODS: We performed a cross-sectional study utilizing the 1996 Nationwide Inpatient Sample, a data set of all hospitalizations from a 20% sample of nonfederal US hospitals. The study population included 71 130 patients aged 40 years or older with an acute exacerbation of COPD at hospital discharge. The primary outcome assessed was in-hospital mortality. RESULTS: In-hospital mortality for patients with an acute exacerbation of COPD was 2.5%. Multivariable analyses identified older age, male sex, higher income, nonroutine admission sources, and more comorbid conditions as independent risk factors for in-hospital mortality. CONCLUSIONS: Mortality during hospitalization in this nationwide sample of patients with acute exacerbations of COPD was lower than that of previous studies of select populations. This estimate should provide optimism to both clinicians and patients regarding prognoses from COPD exacerbations requiring hospitalization. Our results indicate that the use of administrative data can help to identify subsets of patients with acute exacerbations of COPD that are at higher risk of in-hospital mortality.