Variation exists in rates of admission to intensive care units for heart failure patients across hospitals in the United States

Association between intra-arterial catheterization and mortality of acute heart failure patients without shock in ICU: A retrospective study

Background

Acute heart failure necessitates intensive care, and arterial catheterization is a commonly performed invasive procedure in the intensive care unit (ICU). We aimed to investigate the association between arterial catheterization and outcomes in acute heart failure patients without shock.

Methods

We utilized MIMIC-IV database records for acute heart failure patients at Beth Israel Deaconess Medical Center from 2008 to 2019. Employing doubly robust estimation, we examined the relationship between arterial catheterization and outcomes, including 28-day, 90-day, in-hospital mortality, and ICU-free days within 28 days.

Results

Of 6936 patients identified, 2078 met inclusion criteria; 347 underwent arterial catheterization during their ICU stay. We observed no significant difference in 28-day mortality (odds ratio [OR]: 0.61, 95 % confidence interval [CI]: 0.31-1.21, P = 0.155), though catheterization was associated with reduced in-hospital mortality (OR: 0.41, 95 % CI: 0.14-0.65, P = 0.02). No significant effects were observed on 90-day mortality or ICU-free days within 28 days.

Conclusion

Our findings suggest that arterial catheterization is not associated with 28- and 90-day mortality rates in acute heart failure patients without shock but is linked to lower in-hospital mortality. Additional research and consensus are required to determine the appropriate utilization of arterial catheterization in patients.

The relationship between serum anion gap levels and short-, medium-, and long-term all-cause mortality in ICU patients with congestive heart failure: a retrospective cohort study

BACKGROUND

There hasn't been research done on the connection between serum anion gap (AG) levels and long-, medium-, and short-term all-cause mortality in congestive heart failure (CHF) patients. This study aims to investigate the association between serum anion gap levels and all-cause mortality in CHF patients after adjusting for other covariates.

METHODS

For each patient, we gather demographic information, comorbidities, laboratory results, vital signs, and scoring data using the ICU (Intensive Care Unit) Admission Scoring System from the MIMIC-III database. The connection between baseline AG and long-, medium-, and short-term all-cause mortality in critically ill congestive heart failure patients was investigated using Kaplan-Meier survival curves, subgroup analysis, restricted cubic spline, and Cox proportional risk analysis.

RESULTS

4840 patients with congestive heart failure in total were included in this study. With a mean age of 72.5 years, these patients had a gender split of 2567 males and 2273 females. After adjusting for other covariates, a multiple regression analysis revealed that, in critically ill patients with congestive heart failure, all-cause mortality increased significantly with rising AG levels. In the fully adjusted model, we discovered that AG levels were strongly correlated with 4-year, 365-day, 90-day, and 30-day all-cause mortality in congestive heart failure patients with HRs (95% CI) of 1.06 (1.04, 1.08); 1.08 (1.05, 1.10); and 1.08 (1.05, 1.11) (p-value < 0.05). Our subgroup analysis's findings demonstrated a high level of consistency and reliability. K-M survival curves demonstrate that high serum AG levels are associated with a lower survival probability.

CONCLUSION

Our research showed the association between CHF patients' all-cause mortality and anion gap levels was non-linear. Elevated anion gap levels are associated with an increased risk of long-, medium-, and short-term all-cause death in patients with congestive heart failure. Continuous monitoring of changes in AG levels may have a clinical predictive role.

The Role of Prognostic Scores in Assessing the Prognosis of Patients Admitted in the Cardiac Intensive Care Unit: Emphasis on Heart Failure Patients

Background/Objectives: Patient care in Cardiac Intensive Care Units (CICU) has evolved but data on patient characteristics and outcomes are sparse. This retrospective observational study aimed to define clinical characteristics and risk factors of CICU patients, their in-hospital and 30-day mortality, and compare it with established risk scores. Methods: Consecutive patients (n = 294, mean age 70 years, 74% males) hospitalized within 15 months were studied; APACHE II, EHMRG, GWTG-HF, and GRACE II were calculated on admission. Results: Most patients were admitted for ACS (48.3%) and acute decompensated heart failure (ADHF) (31.3%). Median duration of hospitalization was 2 days (IQR = 1, 4). In-hospital infection occurred in 20%, 18% needed mechanical ventilation, 10% renal replacement therapy and 4% percutaneous ventricular assist devices (33%, 29%, 20% and 4%, respectively, for ADHF). In-hospital and 30-day mortality was 18% and 11% for all patients (29% and 23%, respectively, for ADHF). Established scores (especially APACHE II) had a good diagnostic accuracy (area under the curve-AUC). In univariate and multivariate analyses in-hospital intubation and infection, history of coronary artery disease, hypotension, uremia and hypoxemia on admission were the most important risk factors. Based on these, a proposed new score showed a diagnostic accuracy of 0.954 (AUC) for in-hospital mortality, outperforming previous scores. Conclusions: Patients are admitted mainly with ACS or ADHF, the latter with worse prognosis. Several patients need advanced support; intubation and infections adversely affect prognosis. Established scores predict mortality satisfactorily, but larger studies are needed to develop CICU-directed scores to identify risk factors, improve prediction, guide treatment and staff training.

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model

BACKGROUND

Clinical notes contain contextualized information beyond structured data related to patients' past and current health status.

OBJECTIVE

This study aimed to design a multimodal deep learning approach to improve the evaluation precision of hospital outcomes for heart failure (HF) using admission clinical notes and easily collected tabular data.

METHODS

Data for the development and validation of the multimodal model were retrospectively derived from 3 open-access US databases, including the Medical Information Mart for Intensive Care III v1.4 (MIMIC-III) and MIMIC-IV v1.0, collected from a teaching hospital from 2001 to 2019, and the eICU Collaborative Research Database v1.2, collected from 208 hospitals from 2014 to 2015. The study cohorts consisted of all patients with critical HF. The clinical notes, including chief complaint, history of present illness, physical examination, medical history, and admission medication, as well as clinical variables recorded in electronic health records, were analyzed. We developed a deep learning mortality prediction model for in-hospital patients, which underwent complete internal, prospective, and external evaluation. The Integrated Gradients and SHapley Additive exPlanations (SHAP) methods were used to analyze the importance of risk factors.

RESULTS

The study included 9989 (16.4%) patients in the development set, 2497 (14.1%) patients in the internal validation set, 1896 (18.3%) in the prospective validation set, and 7432 (15%) patients in the external validation set. The area under the receiver operating characteristic curve of the models was 0.838 (95% CI 0.827-0.851), 0.849 (95% CI 0.841-0.856), and 0.767 (95% CI 0.762-0.772), for the internal, prospective, and external validation sets, respectively. The area under the receiver operating characteristic curve of the multimodal model outperformed that of the unimodal models in all test sets, and tabular data contributed to higher discrimination. The medical history and physical examination were more useful than other factors in early assessments.

CONCLUSIONS

The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.

Correlation between blood albumin and hospital death and long-term death in ICU patients with heart failure: data from the medical information mart for intensive care III database

BACKGROUND

Elevated circulating levels of albumin (ALB) are often associated with improved prognosis in patients with heart failure (HF). However, investigations of its association with hospital death and long-term death in HF patients in the intensive care unit (ICU) are limited.

AIM

We examined whether increased blood ALB levels (first value at admission and maximum and minimum values in the ICU) were related to a greater risk of hospital death and long-term death in ICU patients with HF.

METHODS

For the first time, we analyzed 4084 ICU patients with HF admitted to the ICU in The Medical Information Mart for Intensive Care III (MIMIC-III) database.

RESULTS

Among 4084 HF patients, 774 (18.95%), 1056 (25.86%) and 1720 (42.12%) died in the hospital, within 30 days and 1 year, respectively. We conducted a logistic regression analysis and found significant inverse associations between blood ALB concentration and risk of hospital death, 30-day death and 1-year death when the covariates including age, sex, myocardial infarction (MI), hypertension, diabetes, valvular diseases, atrial fibrillation, stroke and chronic kidney disease (CKD) were adjusted. We additionally used a smooth curve for univariate analysis to establish an association between blood ALB concentration and death risk. Surprisingly, we observed U-shaped correlations between blood ALB concentration and hospital mortality, 30-day mortality and 1-year mortality. We found that the "inflection point" for the blood ALB concentration at the lowest risk of death was 3.5 g/dL. We further observed that a higher blood ALB concentration (albumin-max) did not contribute to a reduced risk of death (hospital death, 30-day death and 1-year death) in HF patients with an albumin concentration >3.5 g/dL.

CONCLUSIONS

A lower blood ALB concentration contributed to a greater risk of hospital death and long-term death in HF patients admitted to the ICU, further suggesting that nutritional support in the ICU is highly important for improving the short-term and long-term mortality of HF patients. However, in HF patients without hypoproteinaemia (>3.5 g/dL), the impact of increased serum ALB on patient prognosis still needs to be demonstrated.

Racial Inequities in Mortality Rate in Hospitalized Children

BACKGROUND AND OBJECTIVES

Racial/ethnic inequities for inpatient mortality in children at a national level in the U.S. have not been explored. The objective of this study was to evaluate differences in inpatient mortality rate among different racial/ethnic groups, using the Kids' Inpatient Database.

METHODS

A cross-sectional study of children of ages greater than 28 days and less than 21 years discharged during 2012 and 2016. Racial/ethnic groups - White, Black, Hispanic, Asian and Pacific Islander and Native Americans were analyzed in two cohorts, Cohort A (all discharges) and Cohort B (ventilated children).

RESULTS

A total of 4,247,604 and 79,116 discharges were included in cohorts A and B, respectively. Univariate analysis showed that the inpatient mortality rate was highest among Asian and Pacific Islander children for both cohorts: A (0.47% [0.42-0.51]), B (10.9% [9.8-12.1]). Regression analysis showed that Asian and Pacific Islander and Black children had increased odds of inpatient mortality compared to White children: A (1.319 [1.162-1.496], 1.178 [1.105-1.257], respectively) and B (1.391 [1.199-1.613], 1.163 [1.079-1.255], respectively). Population-based hospital mortality was highest in Black children (1.17 per 10,000 children).

CONCLUSIONS

Inpatient mortality rates are significantly higher in U.S. children of Asian and Pacific Islander and Black races compared to White children. U.S. population-based metrics such as hospitalization rate, ventilation rate, and hospital mortality rate are highest in Black children. Our data suggest that lower median household income alone may not account for a higher inpatient mortality rate. The causes and prevention of racial and ethnic inequities in hospitalized children need to be explored further.

Interhospital Variation in Admissions Managed With Critical Care Therapies or Invasive Hemodynamic Monitoring in Tertiary Cardiac Intensive Care Units: An Analysis From the Critical Care Cardiology Trials Network Registry

BACKGROUND

Wide interhospital variations exist in cardiovascular intensive care unit (CICU) admission practices and the use of critical care restricted therapies (CCRx), but little is known about the differences in patient acuity, CCRx utilization, and the associated outcomes within tertiary centers.

METHODS

The Critical Care Cardiology Trials Network is a multicenter registry of tertiary and academic CICUs in the United States and Canada that captured consecutive admissions in 2-month periods between 2017 and 2022. This analysis included 17 843 admissions across 34 sites and compared interhospital tertiles of CCRx (eg, mechanical ventilation, mechanical circulatory support, continuous renal replacement therapy) utilization and its adjusted association with in-hospital survival using logistic regression. The Pratt index was used to quantify patient-related and institutional factors associated with CCRx variability.

RESULTS

The median age of the study population was 66 (56-77) years and 37% were female. CCRx was provided to 62.2% (interhospital range of 21.3%-87.1%) of CICU patients. Admissions to CICUs with the highest tertile of CCRx utilization had a greater burden of comorbidities, had more diagnoses of ST-elevation myocardial infarction, cardiac arrest, or cardiogenic shock, and had higher Sequential Organ Failure Assessment scores. The unadjusted in-hospital mortality (median, 12.7%) was 9.6%, 11.1%, and 18.7% in low, intermediate, and high CCRx tertiles, respectively. No clinically meaningful differences in adjusted mortality were observed across tertiles when admissions were stratified by the provision of CCRx. Baseline patient-level variables and institutional differences accounted for 80% and 5.3% of the observed CCRx variability, respectively.

CONCLUSIONS

In a large registry of tertiary and academic CICUs, there was a >4-fold interhospital variation in the provision of CCRx that was primarily driven by differences in patient acuity compared with institutional differences. No differences were observed in adjusted mortality between low, intermediate, and high CCRx utilization sites.

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database

BACKGROUND

There is a continual push for developing accurate predictors for Intensive Care Unit (ICU) admitted heart failure (HF) patients and in-hospital mortality.

OBJECTIVE

The study aimed to utilize transparent machine learning and create hierarchical clustering of key predictors based off of model importance statistics gain, cover, and frequency.

METHODS

Inclusion criteria of complete patient information for in-hospital mortality in the ICU with HF from the MIMIC-III database were randomly divided into a training (n = 941, 80%) and test (n = 235, 20%). A grid search was set to find hyperparameters. Machine Learning with XGBoost were used to predict mortality followed by feature importance with Shapely Additive Explanations (SHAP) and hierarchical clustering of model metrics with a dendrogram and heat map.

RESULTS

Of the 1,176 heart failure ICU patients that met inclusion criteria for the study, 558 (47.5%) were males. The mean age was 74.05 (SD = 12.85). XGBoost model had an area under the receiver operator curve of 0.662. The highest overall SHAP explanations were urine output, leukocytes, bicarbonate, and platelets. Average urine output was 1899.28 (SD = 1272.36) mL/day with the hospital mortality group having 1345.97 (SD = 1136.58) mL/day and the group without hospital mortality having 1986.91 (SD = 1271.16) mL/day. The average leukocyte count in the cohort was 10.72 (SD = 5.23) cells per microliter. For the hospital mortality group the leukocyte count was 13.47 (SD = 7.42) cells per microliter and for the group without hospital mortality the leukocyte count was 10.28 (SD = 4.66) cells per microliter. The average bicarbonate value was 26.91 (SD = 5.17) mEq/L. Amongst the group with hospital mortality the average bicarbonate value was 24.00 (SD = 5.42) mEq/L. Amongst the group without hospital mortality the average bicarbonate value was 27.37 (SD = 4.98) mEq/L. The average platelet value was 241.52 platelets per microliter. For the group with hospital mortality the average platelet value was 216.21 platelets per microliter. For the group without hospital mortality the average platelet value was 245.47 platelets per microliter. Cluster 1 of the dendrogram grouped the temperature, platelets, urine output, Saturation of partial pressure of Oxygen (SPO2), Leukocyte count, lymphocyte count, bicarbonate, anion gap, respiratory rate, PCO2, BMI, and age as most similar in having the highest aggregate gain, cover, and frequency metrics.

CONCLUSION

Machine Learning models that incorporate dendrograms and heat maps can offer additional summaries of model statistics in differentiating factors between in patient ICU mortality in heart failure patients.

Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart failure

Objective

Risk stratification of patients with congestive heart failure (HF) is vital in clinical practice. The aim of this study was to construct a machine learning model to predict the in-hospital all-cause mortality for intensive care unit (ICU) patients with HF.

Methods

eXtreme Gradient Boosting algorithm (XGBoost) was used to construct a new prediction model (XGBoost model) from the Medical Information Mart for Intensive Care IV database (MIMIC-IV) (training set). The eICU Collaborative Research Database dataset (eICU-CRD) was used for the external validation (test set). The XGBoost model performance was compared with a logistic regression model and an existing model (Get with the guideline-Heart Failure model) for mortality in the test set. Area under the receiver operating characteristic cure and Brier score were employed to evaluate the discrimination and the calibration of the three models. The SHapley Additive exPlanations (SHAP) value was applied to explain XGBoost model and calculate the importance of its features.

Results

The total of 11,156 and 9,837 patients with congestive HF from the training set and test set, respectively, were included in the study. In-hospital all-cause mortality occurred in 13.3% (1,484/11,156) and 13.4% (1,319/9,837) of patients, respectively. In the training set, of 17 features with the highest predictive value were selected into the models with LASSO regression. Acute Physiology Score III (APS III), age and Sequential Organ Failure Assessment (SOFA) were strongest predictors in SHAP. In the external validation, the XGBoost model performance was superior to that of conventional risk predictive methods, with an area under the curve of 0.771 (95% confidence interval, 0.757–0.784) and a Brier score of 0.100. In the evaluation of clinical effectiveness, the machine learning model brought a positive net benefit in the threshold probability of 0%–90%, prompting evident competitiveness compare to the other two models. This model has been translated into an online calculator which is accessible freely to the public (https://nkuwangkai-app-for-mortality-prediction-app-a8mhkf.streamlit.app).

Conclusion

This study developed a valuable machine learning risk stratification tool to accurately assess and stratify the risk of in-hospital all-cause mortality in ICU patients with congestive HF. This model was translated into a web-based calculator which access freely.

Among-Hospital Variation in Intensive Care Unit Admission Practices and Associated Outcomes for Patients with Acute Respiratory Failure

Rationale: We have previously shown that hospital strain is associated with intensive care unit (ICU) admission and that ICU admission, compared with ward admission, may benefit certain patients with acute respiratory failure (ARF). Objectives: To understand how strain-process-outcomes relationships in patients with ARF may vary among hospitals and what hospital practice differences may account for such variation. Methods: We examined high-acuity patients with ARF who did not require mechanical ventilation or vasopressors in the emergency department (ED) and were admitted to 27 U.S. hospitals from 2013 to 2018. Stratifying by hospital, we compared hospital strain-ICU admission relationships and hospital length of stay (LOS) and mortality among patients initially admitted to the ICU versus the ward using hospital strain as a previously validated instrumental variable. We also surveyed hospital practices and, in exploratory analyses, evaluated their associations with the above processes and outcomes. Results: There was significant among-hospital variation in ICU admission rates, in hospital strain-ICU admission relationships, and in the association of ICU admission with hospital LOS and hospital mortality. Overall, ED patients with ARF (n = 45,339) experienced a 0.82-day shorter median hospital LOS if admitted initially to the ICU compared with the ward, but among the 27 hospitals (n = 224-3,324), this effect varied from 5.85 days shorter (95% confidence interval [CI], -8.84 to -2.86; P < 0.001) to 4.38 days longer (95% CI, 1.86-6.90; P = 0.001). Corresponding ranges for in-hospital mortality with ICU compared with ward admission revealed odds ratios from 0.08 (95% CI, 0.01-0.56; P < 0.007) to 8.89 (95% CI, 1.60-79.85; P = 0.016) among patients with ARF (pooled odds ratio, 0.75). In exploratory analyses, only a small number of measured hospital practices-the presence of a sepsis ED disposition guideline and maximum ED patient capacity-were potentially associated with hospital strain-ICU admission relationships. Conclusions: Hospitals vary considerably in ICU admission rates, the sensitivity of those rates to hospital capacity strain, and the benefits of ICU admission for patients with ARF not requiring life support therapies in the ED. Future work is needed to more fully identify hospital-level factors contributing to these relationships.

Integrated rapid-cycle comparative effectiveness trials using flexible point of care randomisation in electronic health record systems

Whilst the Randomised Controlled Trial remains the gold standard for deriving robust causal estimates of treatment efficacy, too often a traditional design proves prohibitively expensive or cumbersome when it comes to assessing questions regarding the comparative effectiveness of routinely used treatments. As a result, patients experience variation in practice as clinicians lack the evidence needed to personalise treatments effectively. This variation may be classified as unwarranted, where existing evidence is ignored, or legitimate where in the absence of evidence, clinicians rely on experience, expert opinion, and inferred principles from basic science to make decisions. We argue that within the right ethical and technological framework, legitimate variation can be transformed into a mechanism for evidence generation and learning. Learning Health Systems which harness existing variation in practice, represent a novel approach for generating evidence from everyday clinical practice. The development of these systems has gained traction due to the increased availability of modern Electronic Health Record Systems. However, despite their promise, overcoming hurdles to successfully integrating clinical trials within Learning Health Systems has proven challenging. This article describes the origins of integrated clinical trials and explores two main barriers to their further implementation - how best to obtain informed consent from patients to participate in routine comparative effectiveness research, and how to automate and integrate randomisation into a clinical workflow. Having described these barriers, we present a potential solution in the form of a research pipeline using a novel form of flexible point-of-care randomisation to allow clinicians and patients to participate in studies where there is clinical equipoise.

Association of thiamine administration and prognosis in critically ill patients with heart failure

Background: Thiamine deficiency is common in patients with heart failure, and thiamine supplement can benefit these patients. However, the association between thiamine administration and prognosis among critically ill patients with heart failure remains unclear. Thus, this study aims to prove the survival benefit of thiamine use in critically ill patients with heart failure. Methods: A retrospective cohort analysis was performed on the basis of the Medical Information Mart of Intensive Care-Ⅳ database. Critically ill patients with heart failure were divided into the thiamine and non-thiamine groups depending on whether they had received thiamine therapy or not during hospitalization. The association between thiamine supplement and in-hospital mortality was assessed by using the Kaplan-Meier (KM) method and Cox proportional hazard models. A 1:1 nearest propensity-score matching (PSM) and propensity score-based inverse probability of treatment weighting (IPW) were also performed to ensure the robustness of the findings. Results: A total of 7,021 patients were included in this study, with 685 and 6,336 in the thiamine and non-thiamine groups, respectively. The kaplan-meier survival curves indicated that the thiamine group had a lower in-hospital mortality than the none-thiamine group. After adjusting for various confounders, the Cox regression models showed significant beneficial effects of thiamine administration on in-hospital mortality among critically ill patients with heart failure with a hazard ratio of 0.78 (95% confidence interval: 0.67-0.89) in the fully adjusted model. propensity-score matching and probability of treatment weighting analyses also achieved consistent results. Conclusion: Thiamine supplement is associated with a decreased risk of in-hospital mortality in critically ill patients with heart failure who are admitted to the ICU. Further multicenter and well-designed randomized controlled trials with large sample sizes are necessary to validate this finding.

Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases

Background

Heart failure (HF) combined with hypertension is an extremely important cause of in-hospital mortality, especially for the intensive care unit (ICU) patients. However, under intense working pressure, the medical staff are easily overwhelmed by the large number of clinical signals generated in the ICU, which may lead to treatment delay, sub-optimal care, or even wrong clinical decisions. Individual risk stratification is an essential strategy for managing ICU patients with HF combined with hypertension. Artificial intelligence, especially machine learning (ML), can develop superior models to predict the prognosis of these patients. This study aimed to develop a machine learning method to predict the 28-day mortality for ICU patients with HF combined with hypertension.

Methods

We enrolled all critically ill patients with HF combined with hypertension in the Medical Information Mart for IntensiveCare Database-IV (MIMIC-IV, v.1.4) and the eICU Collaborative Research Database (eICU-CRD) from 2008 to 2019. Subsequently, MIMIC-IV was divided into training cohort and testing cohort in an 8:2 ratio, and eICU-CRD was designated as the external validation cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression with internal tenfold cross-validation was used for data dimension reduction and identifying the most valuable predictive features for 28-day mortality. Based on its accuracy and area under the curve (AUC), the best model in the validation cohort was selected. In addition, we utilized the Shapley Additive Explanations (SHAP) method to highlight the importance of model features, analyze the impact of individual features on model output, and visualize an individual’s Shapley values.

Results

A total of 3,458 and 6582 patients with HF combined with hypertension in MIMIC-IV and eICU-CRD were included. The patients, including 1,756 males, had a median (Q1, Q3) age of 75 (65, 84) years. After selection, 22 out of a total of 58 clinical parameters were extracted to develop the machine-learning models. Among four constructed models, the Neural Networks (NN) model performed the best predictive performance with an AUC of 0.764 and 0.674 in the test cohort and external validation cohort, respectively. In addition, a simplified model including seven variables was built based on NN, which also had good predictive performance (AUC: 0.741). Feature importance analysis showed that age, mechanical ventilation (MECHVENT), chloride, bun, anion gap, paraplegia, rdw (RDW), hyperlipidemia, peripheral capillary oxygen saturation (SpO₂), respiratory rate, cerebrovascular disease, heart rate, white blood cell (WBC), international normalized ratio (INR), mean corpuscular hemoglobin concentration (MCHC), glucose, AIDS, mean corpuscular volume (MCV), N-terminal pro-brain natriuretic peptide (Npro. BNP), calcium, renal replacement therapy (RRT), and partial thromboplastin time (PTT) were the top 22 features of the NN model with the greatest impact. Finally, after hyperparameter optimization, SHAP plots were employed to make the NN-based model interpretable with an analytical description of how the constructed model visualizes the prediction of death.

Conclusion

We developed a predictive model to predict the 28-day mortality for ICU patients with HF combined with hypertension, which proved superior to the traditional logistic regression analysis. The SHAP method enables machine learning models to be more interpretable, thereby helping clinicians to better understand the reasoning behind the outcome and assess in-hospital outcomes for critically ill patients.

Serum albumin and the short-term mortality in individuals with congestive heart failure in intensive care unit: an analysis of MIMIC

Decreased albumin levels are common in congestive heart failure (CHF), but little is known about its role in mortality risk in CHF. This study developed a cohort prediction model based on 7121 individuals with heart failure to evaluate the short-term mortality and prognostic role of albumin in patients with CHF. The cohort was from intensive care unit between 2001 and 2012 in a publicly available clinical database in intensive care called MIMIC III. We used a generalized additive model to determine the nonlinear correlation between serum albumin and 14th day, 28th day and 90th day all-cause mortality in patients with heart failure. The results showed that serum albumin is an independent risk factor for 14th, 28th and 90th day all-cause mortality, and has a linear relationship with all-cause mortality in congestive heart failure. Cox regression analysis using restricted cubic spline with albumin as continuous parameter showed that the decrease of albumin level is directly related to the increase of mortality (14th day mortality: hazard ratio [HR], 0.65 [95% CI, 0.58 to 0.73]); 28th day mortality: HR, 0.56 [95% CI, 0.51 to 0.63]; 90th day mortality: HR, 0.52 [95% CI, 0.47 to 0.57]; P for trend < 0.001). The multivariate adjusted association between albumin (as a continuous variable) and all-cause mortality on the 90th days is mixed by ARDS [HR, 0.64, 95% CI (0.47–0.87), P = 0.005]. The all-cause mortality on the 90th day predicted better clinical results with the all-cause mortality on the 14th day.

Embedded point of care randomisation for evaluating comparative effectiveness questions: PROSPECTOR-critical care feasibility study protocol

INTRODUCTION

Many routinely administered treatments lack evidence as to their effectiveness. When treatments lack evidence, patients receive varying care based on the preferences of clinicians. Standard randomised controlled trials are unsuited to comparisons of different routine treatment strategies, and there remains little economic incentive for change.Integrating clinical trial infrastructure into electronic health record systems offers the potential for routine treatment comparisons at scale, through reduced trial costs. To date, embedded trials have automated data collection, participant identification and eligibility screening, but randomisation and consent remain manual and therefore costly tasks.This study will investigate the feasibility of using computer prompts to allow flexible randomisation at the point of clinical decision making. It will compare the effectiveness of two prompt designs through the lens of a candidate research question-comparing liberal or restrictive magnesium supplementation practices for critical care patients. It will also explore the acceptability of two consent models for conducting comparative effectiveness research.

METHODS AND ANALYSIS

We will conduct a single centre, mixed-methods feasibility study, aiming to recruit 50 patients undergoing elective surgery requiring postoperative critical care admission. Participants will be randomised to either 'Nudge' or 'Preference' designs of electronic point-of-care randomisation prompt, and liberal or restrictive magnesium supplementation.We will judge feasibility through a combination of study outcomes. The primary outcome will be the proportion of prompts displayed resulting in successful randomisation events (compliance with the allocated magnesium strategy). Secondary outcomes will evaluate the acceptability of both prompt designs to clinicians and ascertain the acceptability of pre-emptive and opt-out consent models to patients.

ETHICS AND DISSEMINATION

This study was approved by Riverside Research Ethics Committee (Ref: 21/LO/0785) and will be published on completion.

TRIAL REGISTRATION NUMBER

NCT05149820.

Predictors for Withdrawal of Life-Sustaining Therapies in Patients With Traumatic Brain Injury: A Retrospective Trauma Quality Improvement Program Database Study

Many patients with severe traumatic brain injuries (TBIs) undergo withdrawal of life-sustaining therapies (WLSTs) or transition to comfort measures, but noninjury factors that influence this decision have not been well characterized. We hypothesized that WLST would be associated with institutional and geographic noninjury factors. All patients with a head Abbreviated Injury Scale score ≥3 were identified from 2016 Trauma Quality Improvement Program data. We analyzed factors that might be associated with WLST, including procedure type, age, sex, race, insurance, Glasgow Coma Scale score, mechanism of injury, geographic region, and institutional size and teaching status. Adjusted logistic regression was performed to examine factors associated with WLST. Sixty-nine thousand fifty-three patients were identified: 66% male, 77% with isolated TBI, and 7.8% had WLST. The median age was 56 years (34-73). A positive correlation was found between increasing age and WLST. Women were less likely to undergo WLST than men (odds ratio 0.91 [0.84-0.98]) and took more time to for WLST (3 vs 2 days, P < .001). African Americans underwent WLST at a significantly lower rate (odds ratio 0.66 [0.58-0.75]). Variations were also discovered based on US region, hospital characteristics, and neurosurgical procedures. WLST in severe TBI is independently associated with noninjury factors such as sex, age, race, hospital characteristics, and geographic region. The effect of noninjury factors on these decisions is poorly understood; further study of WLST patterns can aid health care providers in decision making for patients with severe TBI.

Association of ICU Admission and Outcomes in Sepsis and Acute Respiratory Failure

Rationale: Many decisions to admit patients to the ICU are not grounded in evidence regarding who benefits from such triage, straining ICU capacity and limiting its cost-effectiveness. Objectives: To measure the benefits of ICU admission for patients with sepsis or acute respiratory failure. Methods: At 27 United States hospitals across two health systems from 2013 to 2018, we performed a retrospective cohort study using two-stage instrumental variable quantile regression with a strong instrument (hospital capacity strain) governing ICU versus ward admission among high-acuity patients (i.e., laboratory-based acute physiology score v2 ⩾ 100) with sepsis and/or acute respiratory failure who did not require mechanical ventilation or vasopressors in the emergency department. Measurements and Main Results: Among patients with sepsis (n = 90,150), admission to the ICU was associated with a 1.32-day longer hospital length of stay (95% confidence interval [CI], 1.01-1.63; P < 0.001) (when treating deaths as equivalent to long lengths of stay) and higher in-hospital mortality (odds ratio, 1.48; 95% CI, 1.13-1.88; P = 0.004). Among patients with respiratory failure (n = 45,339), admission to the ICU was associated with a 0.82-day shorter hospital length of stay (95% CI, -1.17 to -0.46; P < 0.001) and reduced in-hospital mortality (odds ratio, 0.75; 95% CI, 0.57-0.96; P = 0.04). In sensitivity analyses of length of stay, excluding, ignoring, or censoring death, results were similar in sepsis but not in respiratory failure. In subgroup analyses, harms of ICU admission for patients with sepsis were concentrated among older patients and those with fewer comorbidities, and the benefits of ICU admission for patients with respiratory failure were concentrated among older patients, highest-acuity patients, and those with more comorbidities. Conclusions: Among high-acuity patients with sepsis who did not require life support in the emergency department, initial admission to the ward, compared with the ICU, was associated with shorter length of stay and improved survival, whereas among patients with acute respiratory failure, triage to the ICU compared with the ward was associated with improved survival.

Association of a Novel Index of Hospital Capacity Strain with Admission to Intensive Care Units

Rationale: Prior approaches to measuring healthcare capacity strain have been constrained by using individual care units, limited metrics of strain, or general, rather than disease-specific, populations.Objectives: We sought to develop a novel composite strain index and measure its association with intensive care unit (ICU) admission decisions and hospital outcomes.Methods: Using more than 9.2 million acute care encounters from 27 Kaiser Permanente Northern California and Penn Medicine hospitals from 2013 to 2018, we deployed multivariable ridge logistic regression to develop a composite strain index based on hourly measurements of 22 capacity-strain metrics across emergency departments, wards, step-down units, and ICUs. We measured the association of this strain index with ICU admission and clinical outcomes using multivariable logistic and quantile regression.Results: Among high-acuity patients with sepsis (n = 90,150) and acute respiratory failure (ARF; n = 45,339) not requiring mechanical ventilation or vasopressors, strain at the time of emergency department disposition decision was inversely associated with the probability of ICU admission (sepsis: adjusted probability ranging from 29.0% [95% confidence interval, 28.0-30.0%] at the lowest strain index decile to 9.3% [8.7-9.9%] at the highest strain index decile; ARF: adjusted probability ranging from 47.2% [45.6-48.9%] at the lowest strain index decile to 12.1% [11.0-13.2%] at the highest strain index decile; P < 0.001 at all deciles). Among subgroups of patients who almost always or never went to the ICU, strain was not associated with hospital length of stay, mortality, or discharge disposition (all P ≥ 0.13). Strain was also not meaningfully associated with patient characteristics.Conclusions: Hospital strain, measured by a novel composite strain index, is strongly associated with ICU admission among patients with sepsis and/or ARF. This strain index fulfills the assumptions of a strong within-hospital instrumental variable for quantifying the net benefit of admission to the ICU for patients with sepsis and/or ARF.

Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database

OBJECTIVE

The predictors of in-hospital mortality for intensive care units (ICUs)-admitted heart failure (HF) patients remain poorly characterised. We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients.

DESIGN

A retrospective cohort study.

SETTING AND PARTICIPANTS

Data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Data on 1177 heart failure patients were analysed.

METHODS

Patients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation (n=825, 70%) and a validation (n=352, 30%) group. Independent risk factors for in-hospital mortality were screened using the extreme gradient boosting (XGBoost) and the least absolute shrinkage and selection operator (LASSO) regression models in the derivation sample. Multivariate logistic regression analysis was used to build prediction models in derivation group, and then validated in validation cohort. Discrimination, calibration and clinical usefulness of the predicting model were assessed using the C-index, calibration plot and decision curve analysis. After pairwise comparison, the best performing model was chosen to build a nomogram according to the regression coefficients.

RESULTS

Among the 1177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression and Get With the Guidelines-Heart Failure (GWTG-HF) risk score models showed acceptable discrimination. The XGBoost and LASSO regression models also showed good calibration. In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-HF risk score model (p<0.05). The XGBoost model was chosen as our final model for its more concise and wider net benefit threshold probability range and was presented as the nomogram.

CONCLUSIONS

Our nomogram enabled good prediction of in-hospital mortality in ICU-admitted HF patients, which may help clinical decision-making for such patients.

Association between early intensive care or coronary care unit admission and post-discharge performance of activities of daily living in patients with acute decompensated heart failure

The management of acute decompensated heart failure often requires intensive care. However, the effects of early intensive care unit/coronary care unit admission on activities of daily living (ADL) in acute decompensated heart failure patients have not been precisely evaluated. Thus, we retrospectively assessed the association between early intensive care unit admission and post-discharge ADL performance in these patients. Acute decompensated heart failure patients (New York Heart Association I–III) admitted on emergency between April 1, 2014, and December 31, 2018, were selected from the Diagnosis Procedure Combination database and divided into intensive care unit/coronary care unit (ICU) and general ward (GW) groups according to the hospitalization type on admission day 1. The propensity score was calculated to create matched cohorts where admission style (intensive care unit/coronary care unit admission) was independent of measured baseline confounding factors, including ADL at admission. The primary outcome was ADL performance level at discharge (post-ADL) defined according to the Barthel index. Secondary outcomes included length of stay and total hospitalization cost (expense). Overall, 12231 patients were eligible, and propensity score matching created 2985 pairs. After matching, post-ADL was significantly higher in the ICU group than in the GW group [mean (standard deviation), GW vs. ICU: 71.5 (35.3) vs. 78.2 (31.2) points, P<0.001; mean difference: 6.7 (95% confidence interval, 5.1–8.4) points]. After matching, length of stay was significantly shorter and expenses were significantly higher in the ICU group than in the GW group. Stratified analysis showed that the patients with low ADL at admission (Barthel index score <60) were the most benefited from early intensive care unit/coronary care unit admission. Thus, early intensive care unit/coronary care unit admission was associated with improved post-ADL in patients with emergency acute decompensated heart failure admission.

The associations between direct and delayed critical care unit admission with mortality and readmissions among patients with heart failure

BACKGROUND

Although greater than 20% of patients hospitalized with heart failure (HF) are admitted to a critical care unit, associated outcomes, and costs have not been delineated. We determined 30-day mortality, 30-day readmissions, and hospital costs associated with direct or delayed critical care unit admission.

METHODS

In a population-based analysis, we compared HF patients who were admitted to critical care directly from the emergency department (direct), after initial ward admission (delayed), or never admitted to critical care during their hospital stay (ward-only).

RESULTS

Among 178,997 HF patients (median age 80 [IQR 71-86] years, 49.6% men) 36,175 (20.2%) were admitted to critical care during their hospitalization (April 2003 to March 2018). Critical care patients were admitted directly from the emergency department (direct, 81.9%) or after initial ward admission (delayed, 18.1%). Multivariable-adjusted hazard ratios (HR) for all-cause 30-day mortality were: 1.69 for direct (95% confidence interval [CI]; 1.55, 1.84) and 4.92 for delayed (95% CI; 4.26, 5.68) critical care-admitted compared to ward-only patients. Multivariable-adjusted repeated events analysis demonstrated increased risk for all-cause 30-day readmission with both direct (HR 1.04, 95% CI; 1.01, 1.08, P = .013) and delayed critical care unit admissions (HR 1.20, 95% CI; 1.13, 1.28, P < .001). Median 30-day costs were $12,163 for direct admissions, $20,173 for delayed admissions, and $9,575 for ward-only patients (P < .001).

CONCLUSIONS

While critical care unit admission indicates increased risk of mortality and readmission at 30 days, those who experienced delayed critical care unit admission exhibited the highest risk of death and highest costs of care.

Cost of inpatient heart failure care and 30-day readmissions in the United States

BACKGROUND

Heart failure hospitalizations are a major financial cost to healthcare systems. This study aimed to evaluate the costs associated with inpatient hospitalization.

METHODS

Patients with a primary diagnosis of heart failure during a hospital admission between 2010 and 2014 in the U.S. Nationwide Readmission Database were included. The primary outcome was total cost defined by direct cost of index admission and first readmission within 30-days.

RESULTS

A total of 2,645,336 patients with primary heart failure were included in the analysis. The mean ± SD total cost overall was $13,807 ± 24,145; with mean total costs of $15,618 ± 25,264 for patients with 30-day readmission and $11,845 ± 22,710 for patients without a readmission. The comorbidities strongly associated with increased cost were pulmonary circulatory disorder (OR 26.24 95% CI 20.06-34.33), valvular heart disease (OR 25.42 95% CI 20.65-31.28) and bleeding (OR 5.96 95% CI 5.47-6.50). Among hospitalized patients, 12.6% underwent an invasive diagnostic procedure or treatment. The mean cost for patients without invasive care was $10,995. This increased by $129,547, $119,769, $251,110 and $293,575 for receipt of circulatory support, intra-aortic balloon pump, LV assist device and heart transplant. The greatest mean additional cost annually was associated with receipt of coronary angiogram ($26,282 per person for a total of ($728.5 million) and mechanical ventilation ($54,529 per person for a total of $501.7 million).

CONCLUSION

In conclusion, the costs associated with inpatient heart failure care are significant, and the major contributors to inpatient costs are comorbidities, invasive procedures and readmissions.

Clinical phenotypes, aetiologies, management, and mortality in acute heart failure: a single-institution study in Latin-America

Aims

Little is known regarding acute heart failure (AHF) clinical characteristics and its hospital outcome in Latin America. This study sought to assess the prevalence of, and identify differences among, in‐hospital outcomes in patients hospitalized for AHF who were stratified by clinical phenotype at a hospital in Latin America.

Methods and results

This is a retrospective cohort study of patients with AHF who were hospitalized in the coronary care unit of a Latin American teaching hospital from January 2006 to December 2018. Cox regression analysis was used to identify predictors of mortality. Of 21 042 patients admitted, 7759 (36.6%) had AHF. Their median age was 62 years, and 35% were women. De novo heart failure was seen in 39.4% of patients. Most common was AHF‐associated acute coronary syndromes (ACS‐HF) in 43.0%, decompensated heart failure (DHF) in 33.7%, hypertensive heart failure (HT‐HF) in 11.8%, and cardiogenic shock (CS) in 5.2%. Pulmonary oedema (PO) (3.3%) and right heart failure (RHF) (3.0%) were least frequent. Coronary artery disease was the most frequent aetiology in 56.5% of patients, valvular heart disease in 22.4%, and cardiomyopathies in 12.3%. Other less frequent aetiology included adult congenital heart disease (2.5%), lung diseases (2.1%), acute aortic syndromes (1.4%), pericardial diseases (0.8%), and intracardiac tumours (0.3%). Aetiology could not be established in 1.6% of patients. Before admission, patients with worsening chronic heart failure and reduced ejection fraction were treated with renin–angiotensin system blockers (60.4%), beta‐blockers (42.5%), or spironolactone (34.4%). The percentages of patients given in‐hospital management with intravenous diuretics, vasodilators, inotropes, and vasopressors were 81.2%, 33.4%, 18.9%, and 20.4%, respectively. The overall in‐hospital mortality was 17.9% (71.3%, 43.9%, 23.8%, 14.9%, 13.6%, and 10.1% for CS, PO, RHF, DHF, ACS‐HF, and HT‐HF, respectively; P < 0.0001). Multivariate analysis revealed that PO (hazard ratio [HR] 2.68, 95% confidence interval [CI] 1.73–4.14, P < 0.0001) and CS (HR 3.37, 95% CI 2.12–5.35, P < 0.0001) were independent predictors of in‐hospital mortality. Use of intravenous diuretics was linked to reduction of in‐hospital mortality (HR 0.70, 95% CI 0.59–0.59, P < 0.0001). By contrast, increased in‐hospital mortality was associated with the use of intravenous inotrope or vasopressor (HR 1.49, 95% CI 1.27–1.76 and HR 2.91, 95% CI 2.41–3.51, P < 0.0001, respectively).

Conclusions

Real‐world evidence from a university hospital in Latin America shows that the high mortality among patients with AHF may depend, among other factors, on patients' AHF clinical phenotypes. The clinical characteristics and aetiologies of AHF appear to differ between these data from Mexico and those from European and US registries.

Association of Hospital-Level Intensive Care Unit Use and Outcomes in Older Patients With Isolated Rib Fractures

IMPORTANCE

The optimal level of care for older patients with rib fractures as an isolated injury is unknown.

OBJECTIVES

To characterize interhospital variability in intensive care unit (ICU) vs non-ICU admission of older patients with isolated rib fractures and to evaluate whether greater hospital-level use of ICU admission is associated with improved outcomes.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included trauma patients aged 65 years and older with isolated rib fractures who were admitted to US trauma centers participating in the National Trauma Data Bank between January 1, 2015, and December 31, 2016. Patients were excluded if they had other significant injuries, were intubated or had assisted respirations in the emergency department (ED), or had a Glasgow Coma Scale (GCS) score of less than 9 in the ED. Hospitals with fewer than 10 eligible patients were excluded. Data analysis was conducted from May 2019 through September 2020.

EXPOSURES

Admission to the ICU.

MAIN OUTCOMES AND MEASURES

Composite of unplanned intubation, pneumonia, or death during hospitalization.

RESULTS

Among 23 951 patients (11 066 [46.2%] women; mean [SD] age, 77.0 [7.2] years) at 573 hospitals, the median (interquartile range) proportion of ICU use was 16.7% (7.4%-32.0%), but this varied from a low of 0% to a high of 91.9%. The composite outcome occurred in 787 patients (3.3%), with unplanned intubation in 317 (1.3%), pneumonia in 180 (0.8%), and death in 451 (1.9%). Accounting for the hierarchical nature of the data and adjusting for propensity scores reflecting factors associated with ICU admission, receiving care at a hospital with the greatest ICU use (quartile 4), compared with a hospital with the lowest ICU use, was associated with decreased likelihood of the composite outcome (adjusted odds ratio, 0.71; 95% CI, 0.55-0.92).

CONCLUSIONS AND RELEVANCE

In this study, admission location of older patients with isolated rib fractures was variable across hospitals, but hospitalization at a center with greater ICU use was associated with improved outcomes. It may be warranted for hospitals with low ICU use to admit more such patients to an ICU.

Initial and Longitudinal Cost of Surgical Resection for Lung Cancer

BACKGROUND

The longitudinal cost of treating patients with non-small cell lung cancer (NSCLC) undergoing surgical resection has not been evaluated. We describe initial and 4-year resource use and cost for NSCLC patients aged 65 years of age or greater who were treated surgically between 2008 and 2013.

METHODS

Using clinical data for NSCLC resections from The Society of Thoracic Surgeons General Thoracic Surgery Database linked to Medicare claims, resource use and cost of preoperative staging, surgery, and subsequent care through 4 years were examined ($2017). Cost of hospital-based care was estimated using cost-to-charge ratios; professional services and care in other settings were valued using reimbursements. Inverse probability weighting was used to account for administrative censoring. Outcomes were stratified by pathologic stage and by surgical approach for stage I lobectomy patients.

RESULTS

Resection hospitalizations averaged 6 days and cost $31,900. In the first 90 days, costs increased with stage ($12,430 for stage I to $26,350 for stage IV). Costs then declined toward quarterly means more similar among stages. Cumulative costs ranged from $131,032 (stage I) to $205,368 (stage IV). In the stage I lobectomy cohort, patients selected for minimally invasive procedures had lower 4-year costs than did thoracotomy patients ($120,346 versus $136,250).

CONCLUSIONS

The 4-year cost of surgical resection for NSCLC was substantial and increased with pathologic stage. Among stage I lobectomy patients, those selected for minimally invasive surgery had lower costs, particularly through 90 days. Potential avenues for improving the value of surgical resection include judicious use of postoperative intensive care and earlier detection and treatment of disease.

The Novel Coronavirus Disease (COVID-19) and Its Impact on Cardiovascular Disease

The coronavirus disease 2019 (COVID-19) pandemic has placed a significant strain on healthcare providers. As the number of patients continue to surge, healthcare workers are now forced to find different approaches to practicing medicine that may affect patient care. In addition, COVID-19 has many cardiovascular complications that affect the clinical course of patients. In this article, we summarize the cardiovascular impact of COVID-19 and some of the challenges that patients and the healthcare system will face during this pandemic.

Goldilocks, the Three Bears and Intensive Care Unit Utilization: Delivering Enough Intensive Care But Not Too Much. A Narrative Review

Professional societies have developed recommendations for patient triage protocols, but wide variations in triage patterns for many acute conditions exist among hospitals in the United States. Differences in hospitals’ triage patterns can be attributed to factors such as physician behavior, hospital policy and real-time conditions such as intensive care unit capacity. The patient safety concern is that patients evaluated for admission to the intensive care unit during times of high intensive care unit capacity may have adverse outcomes related to delays in care. Because standardization of a national triage policy is not feasible due to differing resources available at each hospital, local guidelines should prevail that take into account hospitals’ local resources. The goal would be to better match intensive care unit bed supply with demand.

Time-Limited Trials Among Critically Ill Patients With Advanced Medical Illnesses to Reduce Nonbeneficial Intensive Care Unit Treatments: Protocol for a Multicenter Quality Improvement Study

BACKGROUND

Invasive intensive care unit (ICU) treatments for patients with advanced medical illnesses and poor prognoses may prolong suffering with minimal benefit. Unfortunately, the quality of care planning and communication between clinicians and critically ill patients and their families in these situations are highly variable, frequently leading to overutilization of invasive ICU treatments. Time-limited trials (TLTs) are agreements between the clinicians and the patients and decision makers to use certain medical therapies over defined periods of time and to evaluate whether patients improve or worsen according to predetermined clinical parameters. For patients with advanced medical illnesses receiving aggressive ICU treatments, TLTs can promote effective dialogue, develop consensus in decision making, and set rational boundaries to treatments based on patients' goals of care.

OBJECTIVE

The aim of this study will be to examine whether a multicomponent quality-improvement strategy that uses protocoled TLTs as the default ICU care-planning approach for critically ill patients with advanced medical illnesses will decrease duration and intensity of nonbeneficial ICU care without changing hospital mortality.

METHODS

This study will be conducted in medical ICUs of three public teaching hospitals in Los Angeles County. In Aim 1, we will conduct focus groups and semistructured interviews with key stakeholders to identify facilitators and barriers to implementing TLTs among ICU patients with advanced medical illnesses. In Aim 2, we will train clinicians to use protocol-enhanced TLTs as the default communication and care-planning approach in patients with advanced medical illnesses who receive invasive ICU treatments. Eligible patients will be those who the treating ICU physicians consider to be at high risk for nonbeneficial treatments according to guidelines from the Society of Critical Care Medicine. ICU physicians will be trained to use the TLT protocol through a curriculum of didactic lectures, case discussions, and simulations utilizing actors as family members in role-playing scenarios. Family meetings will be scheduled by trained care managers. The improvement strategy will be implemented sequentially in the three participating hospitals, and outcomes will be evaluated using a before-and-after study design. Key process outcomes will include frequency, timing, and content of family meetings. The primary clinical outcome will be ICU length of stay. Secondary outcomes will include hospital length of stay, days receiving life-sustaining treatments (eg, mechanical ventilation, vasopressors, and renal replacement therapy), number of attempts at cardiopulmonary resuscitation, frequency of invasive ICU procedures, and disposition from hospitalization.

RESULTS

The study began in August 2017. The implementation of interventions and data collection were completed at two of the three hospitals. As of September 2019, the study was at the postintervention stage at the third hospital. We have completed focus groups with physicians at each medical center (N=29) and interviews of family members and surrogate decision makers (N=18). The study is expected to be completed in the first quarter of 2020, and results are expected to be available in mid-2020.

CONCLUSIONS

The successful completion of the aims in this proposal may identify a systematic approach to improve communication and shared decision making and to reduce nonbeneficial invasive treatments for ICU patients with advanced medical illnesses.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/16301.

Association of Hospital Payment Profiles With Variation in 30-Day Medicare Cost for Inpatients With Heart Failure or Pneumonia

IMPORTANCE

Some uncertainty exists about whether hospital variations in cost are largely associated with differences in case mix.

OBJECTIVE

To establish whether the same patients admitted with the same diagnosis (heart failure or pneumonia) at 2 different hospitals incur different costs associated with the hospital's Medicare payment profile.

DESIGN, SETTING, AND PARTICIPANTS

This observational cohort study used Centers for Medicare & Medicaid Services (CMS) discharge data of patients with a principal diagnosis of heart failure (n = 1615) or pneumonia (n = 708) occurring between July 1, 2013, and June 30, 2016. Patients were individuals aged 65 years or older who were enrolled in Medicare fee-for-service Part A and Part B and were discharged from nonfederal, short-term, acute care or critical access hospitals in the United States. Data were analyzed from March 16, 2018, to September 25, 2019.

MAIN OUTCOMES AND MEASURES

The CMS heart failure and pneumonia payment measure cohorts were divided into 2 random samples. In the first sample, hospitals were classified into payment quartiles for heart failure and pneumonia. In the second sample, patients with 2 admissions for heart failure or pneumonia, one in a lowest-quartile hospital and one in a highest-quartile hospital more than 1 month apart, were identified. Standardized Medicare payments for these patients were compared for the lowest- and the highest-quartile payment hospitals.

RESULTS

The study sample included 1615 patients with heart failure (mean [SD] age, 78.7 [8.0] years; 819 [50.7%] male) and 708 with pneumonia (mean [SD] age, 78.3 [8.0] years; 401 [56.6%] male). The observed 30-day mortality rates for patients among lowest- compared with highest-payment hospitals were not significantly different. The median (interquartile range) hospital 30-day risk-standardized mortality rates were 8.1% (7.7%-8.5%) for heart failure and 11.3% (10.7%-12.1%) for pneumonia. The 30-day episode payment for hospitalization for the same patients at the lowest-payment hospitals was $2118 (95% CI, $1168-$3068; P < .001) lower for heart failure and $2907 (95% CI, $1760-$4054; P < .001) lower for pneumonia than at the highest-payment hospitals. More than half of the difference was associated with the payment during the index hospitalization ($1425 [95% CI, $695-$2154; P < .001] for heart failure and $1659 [95% CI, $731-$2588; P < .001] for pneumonia).

CONCLUSIONS AND RELEVANCE

This study found that the same Medicare beneficiaries who were admitted with the same diagnosis to hospitals with the highest payment profiles incurred higher costs than when they were admitted to hospitals with the lowest payment profiles. The findings suggest that variations in payments to hospitals are, at least in part, associated with the hospitals independently of non-time-varying patient characteristics.

Cost-Effectiveness of Sacubitril/Valsartan in Germany: An Application of the Efficiency Frontier

BACKGROUND

To assess the cost-effectiveness of new treatments in Germany, the efficiency frontier (EF) method has been developed. We compared the cost-effectiveness analysis using international standards and the German methodology, using the heart failure drug sacubitril/valsartan as an example.

METHODS

A previously developed Markov model was adapted to include 4 treatment options: no treatment, enalapril, candesartan, and sacubitril/valsartan. The internationally used incremental cost-effectiveness ratio (ICER) was calculated, as well as cost-effectiveness acceptability curves. Additionally, EFs, net monetary benefits (NMBs), and price-acceptability curves were created according to German guidelines. All analyses were performed from the perspective of the German Statutory Health Insurance.

RESULTS

The base-case ICER for sacubitril/valsartan compared to enalapril is €19  300/quality-adjusted life-year. On the cost-effectiveness acceptability curve, sacubitril/valsartan is most likely to be cost-effective, out of all included comparators, from a hypothetical willingness-to-pay threshold of €18 250/quality-adjusted life-year onward. No EF could be constructed for the base case. Taking the uncertainty of the input parameters into account for the probabilistic sensitivity analysis, a NMB of around -€14 000 was calculated, depending on the outcome considered, with the NMB being zero at a daily price for sacubitril/valsartan ranging from €1.52 to €1.67.

CONCLUSION

We calculated an ICER for Germany, comparable to previously published cost-effectiveness analyses for Europe, which widely concluded sacubitril/valsartan to be cost-effective. Using the German EF approach, a considerable discount needs to be applied before sacubitril/valsartan can be considered cost-effective.

Understanding irresponsible use of intensive care unit resources in the USA

Use of intensive care unit (ICU) resources in the USA far outpaces that of other countries. This increased use is not accompanied by superior clinical outcomes and is at times discordant with patient desires. This Series paper seeks to identify major drivers of ICU resource use in the USA, and to offer steps towards better aligning ICU resource use with clinical needs and patient preferences. After considering several factors, such as organisational, ethical, and economic factors, we suggest that there are four intersecting drivers of irresponsible use of ICU resources in the USA: first, excess ICU bed capacity and a scarcity of data to understand which patients that truly benefit from ICU compared with ward care; second, clinicians misinterpreting the goals and means of patient autonomy; third, an extreme fear of rationing by the general public; and fourth, fee-for-service driven use of advanced medical technologies and procedures that beget ICU expansion.

Incremental costs of high intensive care utilisation in patients hospitalised with heart failure

AIMS

Registries have reported large inter-hospital differences in intensive care unit admission rates for patients with acute heart failure, but little is known about the potential economic impact of over-admission of low-risk patients with heart failure to higher cost intensive care units. We described the variability in intensive care unit admission practices, the provision of critical care therapies, and estimated the potential national cost savings if all hospitals adopted low intensive care unit admission practices for patients admitted with heart failure.

METHODS

Using a national population health dataset, we identified 349,693 heart failure admission hospitalisations with a primary diagnosis of heart failure between 2007 and 2016. Hospitals were categorised as low (first quartile), medium (second and third quartile) and high (fourth quartiles) intensive care unit utilisation.

RESULTS

The mean intensive care unit admission rate was 16.4% (inter-hospital range 0.3-51%) including 5.4% in low, 14.5% in medium and 30% in high utilisation hospitals. Intensive care unit therapies in low, medium and high intensive care unit utilisation hospitals were 54.5%, 45.1% and 24.1% (P<0.001), respectively and the inhospital mortality rate was not significantly different. The proportion of hospital costs incurred by intensive care unit care was 7.8% in low, 19.8% in medium and 28.2% in high (P<0.001) admission hospitals. The potential cost savings of altering intensive care unit utilisation practices for patients with heart failure was CAN$234.8m over the study period.

CONCLUSIONS

In a national cohort of patients hospitalised with heart failure, we observed that low intensive care unit utilisation centres had lower hospital costs with no differences in mortality rates. The development of standardised admission criteria for high-cost and high acuity intensive care unit beds could reduce costs to the healthcare system.

Association of in-hospital resource utilization with post-acute spending in Medicare beneficiaries hospitalized for acute myocardial infarction: a cross-sectional study

BACKGROUND

Efforts to decrease hospitalization costs could increase post-acute care costs. This effect could undermine initiatives to reduce overall episode costs and have implications for the design of health care under alternative payment models.

METHODS

Among Medicare fee-for-service beneficiaries aged ≥65 years hospitalized with acute myocardial infarction (AMI) between July 2010 and June 2013 in the Premier Healthcare Database, we studied the association of in-hospital and post-acute care resource utilization and outcomes by in-hospital cost tertiles.

RESULTS

Among patients with AMI at 326 hospitals, the median (range) of each hospital's mean per-patient in-hospital risk-standardized cost (RSC) for the low, medium, and high cost tertiles were $16,257 ($13,097-$17,648), $18,544 ($17,663-$19,875), and $21,831 ($19,923-$31,296), respectively. There was no difference in the median (IQR) of risk-standardized post-acute payments across cost-tertiles: $5014 (4295-6051), $4980 (4349-5931) and $4922 (4056-5457) for the low (n = 90), medium (n = 98), and high (n = 86) in-hospital RSC tertiles (p = 0.21), respectively. In-hospital and 30-day mortality rates did not differ significantly across the in-hospital RSC tertiles; however, 30-day readmission rates were higher at hospitals with higher in-hospital RSCs: median = 17.5, 17.8, and 18.0% at low, medium, and high in-hospital RSC tertiles, respectively (p = 0.005 for test of trend across tertiles).

CONCLUSIONS

In our study of patients hospitalized with AMI, greater resource utilization during the hospitalization was not associated with meaningful differences in costs or mortality during the post-acute period. These findings suggest that it may be possible for higher cost hospitals to improve efficiency in care without increasing post-acute care utilization or worsening outcomes.

Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital

BACKGROUND

Acute respiratory failure occurs frequently in hospitalized patients and often starts before ICU admission. A risk stratification tool to predict mortality and risk for mechanical ventilation (MV) may allow for earlier evaluation and intervention. We developed and validated an automated electronic health record (EHR)-based model-Accurate Prediction of Prolonged Ventilation (APPROVE)-to identify patients at risk of death or respiratory failure requiring >= 48 h of MV.

METHODS

This was an observational study of adults admitted to four hospitals in 2013 or a fifth hospital in 2017. Clinical data were extracted from the EHRs. The 2013 patients were randomly split 50:50 into a derivation/validation cohort. The qualifying event was death or intubation leading to MV >= 48 h. Random forest method was used in model derivation. APPROVE was calculated retrospectively whenever data were available in 2013, and prospectively every 4 h after hospital admission in 2017. The Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) were calculated at the same times as APPROVE. Clinicians were not alerted except for APPROVE in 2017cohort.

RESULTS

There were 68,775 admissions in 2013 and 2258 in 2017. APPROVE had an area under the receiver operator curve of 0.87 (95% CI 0.85-0.88) in 2013 and 0.90 (95% CI 0.84-0.95) in 2017, which is significantly better than the MEWS and NEWS in 2013 but similar to the MEWS and NEWS in 2017. At a threshold of > 0.25, APPROVE had similar sensitivity and positive predictive value (PPV) (sensitivity 63% and PPV 21% in 2013 vs 64% and 16%, respectively, in 2017). Compared to APPROVE in 2013, at a threshold to achieve comparable PPV (19% at MEWS > 4 and 22% at NEWS > 6), the MEWS and NEWS had lower sensitivity (16% for MEWS and NEWS). Similarly in 2017, at a comparable sensitivity threshold (64% for APPROVE > 0.25 and 67% for MEWS and NEWS > 4), more patients who triggered an alert developed the event with APPROVE (PPV 16%) while achieving a lower false positive rate (FPR 5%) compared to the MEWS (PPV 7%, FPR 14%) and NEWS (PPV 4%, FPR 25%).

CONCLUSIONS

An automated EHR model to identify patients at high risk of MV or death was validated retrospectively and prospectively, and was determined to be feasible for real-time risk identification.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02488174 . Registered on 18 March 2015.

Acute Coronary Syndromes and Heart Failure Critical Care Units Utilization and Outcomes in Teaching and Community Hospitals: A National Population-Based Analysis

Acute coronary syndromes (ACS) and heart failure (HF) are the leading diagnoses in patients admitted to critical care units (CCUs). Little is known about the differences between CCU resource use and outcomes across hospital types. The Canadian Institute for Health Information was used to identify patients hospitalized with primary diagnoses of ACS or HF. CCUs were categorized as teaching, large community, medium community, and small community hospitals. Outcomes included CCU rates of admission, use of critical care therapy/procedures, and in-hospital mortality. Among 204,900 patients hospitalized with ACS or HF, 73,338 (35.8%, hospital range 0% to 81.4%) were admitted to CCUs, and it varied across hospital types: 41.0% in teaching, 30.0% in large, 45.4% in medium, and 30.9% in small community hospitals (P < 0.001). The percentage of patients admitted to CCUs who received critical care therapies in teaching, large, medium, and small hospitals were as follows: 73.6%, 50.9%, 24.6%, and 8.8% (P < 0.0001). Compared with the in-hospital mortality rate for patients admitted to CCUs in teaching hospitals (8.2%), outcomes were worse for CCU patients in large (11.0%, adjusted odds ratio [aOR] 1.50; 95% CI, 1.19-1.90), medium (10.5%, aOR 1.56; 95% CI, 1.27-1.92), and small community hospitals (9.2%, aOR 1.59; 95% CI, 1.20-2.10). Patients admitted with ACS or HF to teaching hospital CCUs had a higher observed use of critical care therapies and lower mortality compared with community hospitals. These differences highlight the need to examine differences in CCU admission thresholds, resource utilization, and outcomes across hospitals types.

Is 30-Day Posthospitalization Mortality Lower Among Racial/Ethnic Minorities?: A Reexamination

BACKGROUND

Multiple studies have reported that risk-adjusted rates of 30-day mortality after hospitalization for an acute condition are lower among blacks compared with whites.

OBJECTIVE

To examine if previously reported lower mortality for minorities, relative to whites, is accounted for by adjustment for do-not-resuscitate status, potentially unconfirmed admission diagnosis, and differential risk of hospitalization.

RESEARCH DESIGN

Using inpatient discharge and vital status data for patients aged 18 and older in California, we examined all admissions from January 1, 2010 to June 30, 2011 for acute myocardial infarction, heart failure, pneumonia, acute stroke, gastrointestinal bleed, and hip fracture and estimated relative risk of mortality for Hispanics, non-Hispanic blacks, non-Hispanic Asians, and non-Hispanic whites. Multiple mortality measures were examined: inpatient, 30-, 90-, and 180 day. Adding census data we estimated population risks of hospitalization and hospitalization with inpatient death.

RESULTS

Across all mortality outcomes, blacks had lower mortality rate, relative to whites even after exclusion of patients with do-not-resuscitate status and potentially unconfirmed diagnosis. Compared with whites, the population risk of hospitalization was 80% higher and risk of hospitalization with inpatient mortality was 30% higher among blacks. Among Hispanics and Asians, disparities varied with mortality measure.

CONCLUSIONS

Lower risk of posthospitalization mortality among blacks, relative to whites, may be associated with higher rate of hospitalizations and differences in unobserved patient acuity. Disparities for Hispanics and Asians, relative to whites, vary with the mortality measure used.

How does prior health status (age, comorbidities and frailty) determine critical illness and outcome?

PURPOSE OF REVIEW

Critical illness has a significant impact on an individual's physical and mental health. However, it is less clear to what degree outcomes after critical illness are due to patients' preexisting characteristics, rather than the critical illness itself. In this review, we summarize recent findings regarding the role of age, comorbidity and frailty on long-term outcomes after critical illness.

RECENT FINDINGS

Age, comorbidity and frailty are all associated with an increased risk of critical illness. Although severity of illness drives the risk of acute mortality, recent data suggest that longer term outcomes are much more closely aligned with prior health status. There are growing data regarding the important role of noncardiovascular comorbidity, including psychiatric illness and obesity, in determining long-term outcomes. Finally, preadmission frailty is associated with poor long-term outcomes after critical illness; further data are needed to evaluate the attributable impact of critical illness on the health trajectories of frail individuals.

SUMMARY

Age, comorbidity and frailty play a critical role in determining the long-term outcomes of patients requiring intensive care.

Intensive Care Unit Admission and Survival among Older Patients with Chronic Obstructive Pulmonary Disease, Heart Failure, or Myocardial Infarction

RATIONALE

Admission to an intensive care unit (ICU) may be beneficial to patients with pneumonia with uncertain ICU needs; however, evidence regarding the association between ICU admission and mortality for other common conditions is largely unknown.

OBJECTIVES

To estimate the relationship between ICU admission and outcomes for hospitalized patients with exacerbation of chronic obstructive pulmonary disease (COPD), exacerbation of heart failure (HF), or acute myocardial infarction (AMI).

METHODS

We performed a retrospective cohort study of all acute care hospitalizations from 2010 to 2012 for U.S. fee-for-service Medicare beneficiaries aged 65 years and older admitted with COPD exacerbation, HF exacerbation, or AMI. We used multivariable adjustment and instrumental variable analysis to assess each condition separately. The instrumental variable analysis used differential distance to a high ICU use hospital (defined separately for each condition) as an instrument for ICU admission to examine marginal patients whose likelihood of ICU admission depended on the hospital to which they were admitted. The primary outcome was 30-day mortality. Secondary outcomes included hospital costs.

RESULTS

Among 1,555,798 Medicare beneficiaries with COPD exacerbation, HF exacerbation, or AMI, 486,272 (31%) were admitted to an ICU. The instrumental variable analysis found that ICU admission was not associated with significant differences in 30-day mortality for any condition. ICU admission was associated with significantly greater hospital costs for HF ($11,793 vs. $9,185, P < 0.001; absolute increase, $2,608 [95% confidence interval, $1,377-$3,840]) and AMI ($19,513 vs. $14,590, P < 0.001; absolute increase, $4,922 [95% confidence interval, $2,665-$7,180]), but not for COPD.

CONCLUSIONS

ICU admission did not confer a survival benefit for patients with uncertain ICU needs hospitalized with COPD exacerbation, HF exacerbation, or AMI. These findings suggest that the ICU may be overused for some patients with these conditions. Identifying patients most likely to benefit from ICU admission may improve health care efficiency while reducing costs.

Separating variability in healthcare practice patterns from random error

Improving the quality of care that patients receive is a major focus of clinical research, particularly in the setting of cardiovascular hospitalization. Quality improvement studies seek to estimate and visualize the degree of variability in dichotomous treatment patterns and outcomes across different providers, whereby naive techniques either over-estimate or under-estimate the actual degree of variation. Various statistical methods have been proposed for similar applications including (1) the Gaussian hierarchical model, (2) the semi-parametric Bayesian hierarchical model with a Dirichlet process prior and (3) the non-parametric empirical Bayes approach of smoothing by roughening. Alternatively, we propose that a recently developed method for density estimation in the presence of measurement error, moment-adjusted imputation, can be adapted for this problem. The methods are compared by an extensive simulation study. In the present context, we find that the Bayesian methods are sensitive to the choice of prior and tuning parameters, whereas moment-adjusted imputation performs well with modest sample size requirements. The alternative approaches are applied to identify disparities in the receipt of early physician follow-up after myocardial infarction across 225 hospitals in the CRUSADE registry.

Hospital Variation in Renal Replacement Therapy for Sepsis in the United States

OBJECTIVES

Acute renal replacement therapy in patients with sepsis has increased dramatically with substantial costs. However, the extent of variability in use across hospitals-and whether greater use is associated with better outcomes-is unknown.

DESIGN

Retrospective cohort study.

SETTING

Nationwide Inpatient Sample in 2011.

PATIENTS

Eighteen years old and older with sepsis and acute kidney injury admitted to hospitals sampled by the Nationwide Inpatient Sample in 2011.

INTERVENTIONS

We estimated the risk- and reliability-adjusted rate of acute renal replacement therapy use for patients with sepsis and acute kidney injury at each hospital. We examined the association between hospital-specific renal replacement therapy rate and in-hospital mortality and hospital costs after adjusting for patient and hospital characteristics.

MEASUREMENTS AND MAIN RESULTS

We identified 293,899 hospitalizations with sepsis and acute kidney injury at 440 hospitals, of which 6.4% (n = 18,885) received renal replacement therapy. After risk and reliability adjustment, the median hospital renal replacement therapy rate for patients with sepsis and acute kidney injury was 3.6% (interquartile range, 2.9-4.5%). However, hospitals in the top quintile of renal replacement therapy use had rates ranging from 4.8% to 13.4%. There was no significant association between hospital-specific renal replacement therapy rate and in-hospital mortality (odds ratio per 1% increase in renal replacement therapy rate: 1.03; 95% CI, 0.99-1.07; p = 0.10). Hospital costs were significantly higher with increasing renal replacement therapy rates (absolute cost increase per 1% increase in renal replacement therapy rate: $1,316; 95% CI, $157-$2,475; p = 0.03).

CONCLUSIONS

Use of renal replacement therapy in sepsis varied widely among nationally sampled hospitals without associated differences in mortality. Improving renal replacement standards for the initiation of therapy for sepsis may reduce healthcare costs without increasing mortality.

Characterization of hemodynamically stable acute heart failure patients requiring a critical care unit admission: Derivation, validation, and refinement of a risk score

BACKGROUND

Most patients with acute heart failure (AHF) admitted to critical care units (CCUs) are low acuity and do not require CCU-specific therapies, suggesting that they could be managed in a lower-cost ward environment. This study identified the predictors of clinical events and the need for CCU-specific therapies in patients with AHF.

METHODS

Model derivation was performed using data from patients in the ASCEND-HF trial cohort (n=7,141), and the Acute Heart Failure Emergency Management community-based registry (n=666) was used to externally validate the model and to test the incremental prognostic utility of 4 variables (heart failure etiology, troponin, B-type natriuretic peptide [BNP], ejection fraction) using net reclassification index and integrated discrimination improvement. The primary outcome was an in-hospital composite of the requirement for CCU-specific therapies or clinical events.

RESULTS

The primary composite outcome occurred in 545 (11.4%) derivation cohort participants (n=4,767) and 7 variables were predictors of the primary composite outcome: body mass index, chronic respiratory disease, respiratory rate, resting dyspnea, hemoglobin, sodium, and blood urea nitrogen (c index=0.633, Hosmer-Lemeshow P=.823). In the validation cohort (n=666), 87 (13.1%) events occurred (c index=0.629, Hosmer-Lemeshow P=.386) and adding ischemic heart failure, troponin, and B-type natriuretic peptide improved model performance (net reclassification index 0.79, 95% CI 0.046-0.512; integrated discrimination improvement 0.014, 95% CI 0.005-0.0238). The final 10-variable clinical prediction model demonstrated modest discrimination (c index=0.702) and good calibration (Hosmer-Lemeshow P=.547).

CONCLUSIONS

We derived, validated, and improved upon a clinical prediction model in an international trial and a community-based cohort of AHF. The model has modest discrimination; however, these findings deserve further exploration because they may provide a more accurate means of triaging level of care for patients with AHF who need admission.

Hospital Contributions to Variability in the Use of ICUs Among Elderly Medicare Recipients

OBJECTIVE

Hospitals vary widely in ICU admission rates across numerous medical diagnoses. The extent to which variability in ICU use is specific to individual diagnoses or is a function of the hospital, regardless of disease, is unknown.

DESIGN

Retrospective cohort study.

SETTING

A total of 1,120 acute care hospitals with ICU capabilities.

PATIENTS

Medicare beneficiaries 65 years old or older admitted for five medical diagnoses (acute myocardial infarction, congestive heart failure, stroke, pneumonia, and chronic obstructive pulmonary disease) and a surgical diagnosis (hip fracture treated with arthroplasty) in 2010.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We used multilevel models to calculate risk- and reliability-adjusted ICU admission rates, examined the correlation in ICU admission rates across diagnosis and calculated intraclass correlation coefficients and median odds ratios to quantify the variability in ICU admission rate that was attributable to hospitals. We also examined the ability of a high ICU-use hospital for one condition to predict high ICU use for other conditions. We identified 348,462 patients with one of the eligible conditions. ICU admission rates were positively correlated within hospitals for included medical diagnoses (r range, 0.38-0.59; p < 0.01). The top hospital quartile of ICU use for congestive heart failure had a sensitivity of 50-60% and specificity of 79-81% for detecting top quartile hospitals for each other conditions. After adjustment for patient and hospital characteristics, hospitals accounted for 17.6% (95% CI, 16.2-19.1%) of variability in ICU admission, corresponding to a median odds ratio of 2.3, compared to 25.8% (95% CI, 24.5-27.1%) and median odds ratio 2.8 for diagnosis. This suggests a patient with median baseline risk of ICU admission would more than double his/her odds of ICU admission if moving to a higher utilizing hospital.

CONCLUSIONS

Hospitals account for a significant proportion of variation independent of measured patient and hospital characteristics, suggesting the need for further work to evaluate the causes of variation at the hospital level and potential consequences of variation across hospitals.

An Economic Evaluation of Sacubitril/Valsartan for Heart Failure Patients in the Netherlands

BACKGROUND

In September 2014, the PARADIGM-HF trial showed the heart failure drug combination sacubitril/valsartan to be superior to enalapril for patients with a reduced ejection fraction.

OBJECTIVES

To determine the incremental cost-effectiveness of sacubitril/valsartan compared with enalapril in the Netherlands using the clinical data from the PARADIGM-HF trial.

METHODS

To compare sacubitril/valsartan and enalapril in a cost-effectiveness study, a Markov model was developed using the effectiveness data from the PARADIGM-HF trial. A health care payer's perspective was applied in the economic evaluation. The developed model was used to evaluate the cost-effectiveness for sacubitril/valsartan at different per diem prices.

RESULTS

The base-case analysis showed that sacubitril/valsartan can be cost-effective at maximum daily costs of €5.50 and €14.14 considering willingness-to-pay thresholds of €20,000 and €50,000 per quality-adjusted life-year (QALY), respectively. Sensitivity analysis demonstrated the robustness of the model, identifying only the price of sacubitril/valsartan and the mortality within the sacubitril/valsartan group as significant drivers of the cost-effectiveness ratio. Sacubitril/valsartan was cost-effective at a willingness-to-pay threshold of €20,000 per QALY (€50,000 per QALY) in more than 80% of the replications with certainty at the price point of €3 (€10).

CONCLUSIONS

Sacubitril/valsartan can be considered a cost-effective treatment at a daily price of €5.25. Unless priced lower than enalapril (<€0.045 per day), sacubitril/valsartan is very unlikely to be cost-saving/dominant.

Outcomes of nighttime refusal of admission to the intensive care unit: The role of the intensivist in triage

PURPOSE

To compare outcomes of patients refused medical intensive care unit (MICU) admission overnight to those refused during the day and to examine the impact of the intensivist in triage.

MATERIALS AND METHODS

Retrospective, observational study of patients refused MICU admission at an urban university hospital.

RESULTS

Of 294 patients, 186 (63.3%) were refused admission overnight compared to 108 (36.7%) refused during the day. Severity-of-illness by the Mortality Probability Model was similar between the two groups (P=.20). Daytime triage refusals were more likely to be staffed by an intensivist (P=.01). After risk-adjustment, daytime refusals had a lower odds of subsequent ICU admission (OR 0.46, 95% CI 0.22-0.95, P=.04) than patients triaged at night. There was no evidence for interaction between time of triage and intensivist staffing of the patient (P=.99).

CONCLUSIONS

Patients refused MICU admission overnight are more likely to be later admitted to an ICU than patients refused during the day. However, the mechanism for this observation does not appear to depend on the intensivist's direct evaluation of the patient. Further investigation into the clinician-specific effects of ICU triage and identification of potentially modifiable hospital triage practices will help to improve both ICU utilization and patient safety.

Risk of Death Influences Regional Variation in Intensive Care Unit Admission Rates among the Elderly in the United States

RATIONALE

The extent to which geographic variability in ICU admission across the United States is driven by patients with lower risk of death is unknown.

OBJECTIVES

To determine whether patients at low to moderate risk of death contribute to geographic variation in ICU admission.

METHODS

Retrospective cohort of hospitalizations among Medicare beneficiaries (age > 64 years) admitted for ten common medical and surgical diagnoses (2004 to 2009). We examined population-adjusted rates of ICU admission per 100 hospitalizations in 304 health referral regions (HRR), and estimated the relative risk of ICU admission across strata of regional ICU and risk of death, adjusted for patient and regional characteristics.

MEASUREMENT AND MAIN RESULTS

ICU admission rates varied nearly two-fold across HRR quartiles (quartile 1 to 4: 13.6, 17.3, 20.0, and 25.2 per 100 hospitalizations, respectively). Observed mortality for patients in regions (quartile 4) with the greatest ICU use was 17% compared to 21% in regions with lowest ICU use (quartile 1) (p<0.001). After adjusting for patient and regional characteristics, including regional differences in ICU, skilled nursing, and long-term acute care bed capacity, individuals' risk of death modified the relationship between regional ICU use and an individual's risk of ICU admission (p for interaction<0.001). Region was least important in predicting ICU admission among patients with high (quartile 4) risk of death (RR 1.27, 95% CI 1.22-1.31, for high versus low ICU use regions), and most important for patients with moderate (quartile 2; RR 1.63, 95% CI 1.53-1.72, quartile 3; RR 1.56 95% CI 1.47-1.65) and low (quartile 1) risk of death (RR 1.50, 95% CI 1.41-1.59).

CONCLUSIONS

There is wide variation in in ICU use by geography, independent of ICU beds and physician supply, for patients with low and moderate risks of death.

Interprovincial Differences in Canadian Coronary Care Unit Resource Use and Outcomes

International registries have reported a wide variation in coronary care unit (CCU) admission rates for patients hospitalized with acute coronary syndrome (ACS) or heart failure (HF). Little is known about variation in Canadian interprovincial use and outcomes. Canadian Institute of Health Information data were used to identify hospitalized patients admitted to a CCU with a primary diagnosis of ACS or HF between April 1, 2007 and March 31, 2013. We examined interprovincial differences in CCU admission rates, use of CCU restricted therapies in the first 2 days of admission, and the association between CCU admission rate and risk-adjusted in-hospital mortality at the provincial level. The CCU admission rate among 220,759 patients hospitalized with ACS and HF was 33%, and this varied significantly across provinces (interprovincial range [IPR] 17%-50%; P < 0.001). A majority (59%; IPR, 48%-84%; P < 0.001) of patients admitted to the CCU did not receive critical care restricted therapies within 2 days. In-hospital mortality also varied across provinces (10%; IPR, 5%-13%; P < 0.001). Although statistically significant (P < 0.001), the correlation between CCU admission rates and provincial risk-adjusted in-hospital mortality was low (r = -0.30). These findings highlight the need for national CCU admission criteria designed to reduce variability and improve health care resource use and outcomes.

Emergency department triage of acute heart failure triggered by pneumonia; when an intensive care unit is needed?

Community acquired pneumonia (CAP) is a frequent triggering factor for decompensation of a chronic cardiac dysfunction, leading to acute heart failure (AHF). Patients with AHF exacerbated by CAP, are often admitted through the emergency department for ICU hospitalization, even though more than half the cases do not warrant any intensive care treatment. Emergency department physicians are forced to make disposition decisions based on subjective criteria, due to lack of evidence-based risk scores for AHF combined with CAP. Currently, the available risk models refer distinctly to either AHF or CAP patients. Extrapolation of data by arbitrarily combining these models, is not validated and can be treacherous. Examples of attempts to apply acuity scales provenient from different disciplines and the resulting discrepancies, are given in this review. There is a need for severity classification tools especially elaborated for use in the emergency department, applicable to patients with mixed AHF and CAP, in order to rationalize the ICU dispositions. This is bound to facilitate the efforts to save both lives and resources.

Role of isolated ultrafiltration in the management of chronic refractory and acute decompensated heart failure

Chronic congestive heart failure (CHF) and acute decompensated heart failure (ADHF) refractory to medical therapy represent therapeutic challenges. In such patients, attempts to reduce pulmonary and systemic congestion frequently produce deterioration of renal function. In studies of patients with chronic severe CHF refractory to medical therapy (including loop diuretics), isolated ultrafiltration was frequently able to relieve congestive symptoms by precise removal of extracellular water and sodium, and in some cases was able to restore responsiveness to loop diuretics. Randomized controlled trials comparing isolated ultrafiltration and medical therapy (mainly loop diuretics) in patients with ADHF failed to demonstrate the superiority of isolated ultrafiltration over diuretic therapy with respect to renal function and mortality. Isolated ultrafiltration reduced length of hospital stay in several studies. At this time, there is insufficient evidence to support the use of isolated ultrafiltration as initial therapy of ADHF.