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Purpose To improve the outcome of the acute respiratory distress syndrome (ARDS), one needs to identify potentially modifiable factors associated with mortality. Methods The large observational study to understand the global impact of severe acute respiratory failure (LUNG SAFE) was an international, multicenter, prospective cohort study of patients with severe respiratory failure, conducted in the winter of 2014 in a convenience sample of 459 ICUs from 50 countries across five continents. A pre-specified secondary aim was to examine the factors associated with outcome. Analyses were restricted to patients (93.1 %) fulfilling ARDS criteria on day 1–2 who received invasive mechanical ventilation. Results 2377 patients were included in the analysis. Potentially modifiable factors associated with increased hospital mortality in multivariable analyses include lower PEEP, higher peak inspiratory, plateau, and driving pressures, and increased respiratory rate. The impact of tidal volume on outcome was unclear. Having fewer ICU beds was also associated with higher hospital mortality. Non-modifiable factors associated with worsened outcome from ARDS included older age, active neoplasm, hematologic neoplasm, and chronic liver failure. Severity of illness indices including lower pH, lower PaO 2 /FiO 2 ratio, and higher non-pulmonary SOFA score were associated with poorer outcome. Of the 578 (24.3 %) patients with a limitation of life-sustaining therapies or measures decision, 498 (86.0 %) died in hospital. Factors associated with increased likelihood of limitation of life-sustaining therapies or measures decision included older age, immunosuppression, neoplasia, lower pH and increased non-pulmonary SOFA scores. Conclusions Higher PEEP, lower peak, plateau, and driving pressures, and lower respiratory rate are associated with improved survival from ARDS. Trial Registration: ClinicalTrials.gov NCT02010073.

In this study, the Medicare Premier pay-for-performance demonstration project had no effect on 30-day mortality among patients hospitalized for acute MI, congestive heart failure, or pneumonia — sobering findings for those who hoped pay for performance would improve outcomes. Tying financial incentives to performance, often referred to as pay for performance, has gained broad acceptance as an approach to improving the quality of health care. 1 – 4 The Centers for Medicare and Medicaid Services (CMS) recently completed a 6-year demonstration of pay for performance for hospitals through the Premier Hospital Quality Incentive Demonstration (HQID), and the Affordable Care Act calls for CMS to expand this program to nearly all U.S. hospitals in 2012. The policy of tying financial incentives to the quality of performance has strong face validity — that is, paying for better care should promote improvements in quality . . .

Multiple major health organisations recommend the use of extracorporeal membrane oxygenation (ECMO) support for COVID-19-related acute hypoxaemic respiratory failure. However, initial reports of ECMO use in patients with COVID-19 described very high mortality and there have been no large, international cohort studies of ECMO for COVID-19 reported to date. We used data from the Extracorporeal Life Support Organization (ELSO) Registry to characterise the epidemiology, hospital course, and outcomes of patients aged 16 years or older with confirmed COVID-19 who had ECMO support initiated between Jan 16 and May 1, 2020, at 213 hospitals in 36 countries. The primary outcome was in-hospital death in a time-to-event analysis assessed at 90 days after ECMO initiation. We applied a multivariable Cox model to examine whether patient and hospital factors were associated with in-hospital mortality. Data for 1035 patients with COVID-19 who received ECMO support were included in this study. Of these, 67 (6%) remained hospitalised, 311 (30%) were discharged home or to an acute rehabilitation centre, 101 (10%) were discharged to a long-term acute care centre or unspecified location, 176 (17%) were discharged to another hospital, and 380 (37%) died. The estimated cumulative incidence of in-hospital mortality 90 days after the initiation of ECMO was 37·4% (95% CI 34·4–40·4). Mortality was 39% (380 of 968) in patients with a final disposition of death or hospital discharge. The use of ECMO for circulatory support was independently associated with higher in-hospital mortality (hazard ratio 1·89, 95% CI 1·20–2·97). In the subset of patients with COVID-19 receiving respiratory (venovenous) ECMO and characterised as having acute respiratory distress syndrome, the estimated cumulative incidence of in-hospital mortality 90 days after the initiation of ECMO was 38·0% (95% CI 34·6–41·5). In patients with COVID-19 who received ECMO, both estimated mortality 90 days after ECMO and mortality in those with a final disposition of death or discharge were less than 40%. These data from 213 hospitals worldwide provide a generalisable estimate of ECMO mortality in the setting of COVID-19. None.

In a randomized trial involving 8014 patients with out-of-hospital cardiac arrest, the use of epinephrine resulted in a significantly higher rate of 30-day survival than placebo but not a higher rate of survival with a favorable neurologic outcome.

Objective To determine the impact of the Hospital Value-Based Purchasing (HVBP) program—the US pay for performance program introduced by Medicare to incentivize higher quality care—on 30 day mortality for three incentivized conditions: acute myocardial infarction, heart failure, and pneumonia.Design Observational study.Setting 4267 acute care hospitals in the United States: 2919 participated in the HVBP program and 1348 were ineligible and used as controls (44 in general hospitals in Maryland and 1304 critical access hospitals across the United States).Participants 2 430 618 patients admitted to US hospitals from 2008 through 2013.Main outcome measures 30 day risk adjusted mortality for acute myocardial infarction, heart failure, and pneumonia using a patient level linear spline analysis to examine the association between the introduction of the HVBP program and 30 day mortality. Non-incentivized, medical conditions were the comparators. A secondary outcome measure was to determine whether the introduction of the HVBP program was particularly beneficial for a subgroup of hospital—poor performers at baseline—that may benefit the most.Results Mortality rates of incentivized conditions in hospitals participating in the HVBP program declined at −0.13% for each quarter during the preintervention period and −0.03% point difference for each quarter during the post-intervention period. For non-HVBP hospitals, mortality rates declined at −0.14% point difference for each quarter during the preintervention period and −0.01% point difference for each quarter during the post-intervention period. The difference in the mortality trends between the two groups was small and non-significant (difference in difference in trends −0.03% point difference for each quarter, 95% confidence interval −0.08% to 0.13% point difference, P=0.35). In no subgroups of hospitals was HVBP associated with better outcomes, including poor performers at baseline.Conclusions Evidence that HVBP has led to lower mortality rates is lacking. Nations considering similar pay for performance programs may want to consider alternative models to achieve improved patient outcomes.

Background The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes. Methods Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients ( N  = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die. Results SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power ( Q 2  = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors. Conclusions An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.

OBJECTIVE:We extend the literature on comorbidity measurement by developing 2 indices, based on the Elixhauser Comorbidity measures, designed to predict 2 frequently reported health outcomesin-hospital mortality and 30-day readmission in administrative data. The Elixhauser measures are commonly used in research as an adjustment factor to control for severity of illness. DATA SOURCES:We used a large analysis file built from all-payer hospital administrative data in the Healthcare Cost and Utilization Project State Inpatient Databases from 18 states in 2011 and 2012. METHODS:The final models were derived with bootstrapped replications of backward stepwise logistic regressions on each outcome. Odds ratios and index weights were generated for each Elixhauser comorbidity to create a single index score per record for mortality and readmissions. Model validation was conducted with c-statistics. RESULTS:Our index scores performed as well as using all 29 Elixhauser comorbidity variables separately. The c-statistic for our index scores without inclusion of other covariates was 0.777 (95% confidence interval, 0.776–0.778) for the mortality index and 0.634 (95% confidence interval, 0.633–0.634) for the readmissions index. The indices were stable across multiple subsamples defined by demographic characteristics or clinical condition. The addition of other commonly used covariates (age, sex, expected payer) improved discrimination modestly. CONCLUSIONS:These indices are effective methods to incorporate the influence of comorbid conditions in models designed to assess the risk of in-hospital mortality and readmission using administrative data with limited clinical information, especially when small samples sizes are an issue.

Background Growing evidence indicates that improved nurse staffing in acute hospitals is associated with lower hospital mortality. Current research is limited to studies using hospital level data or without proper adjustment for confounders which makes the translation to practice difficult. Method In this observational study we analysed retrospectively the control group of a stepped wedge randomised controlled trial concerning 14 medical and 14 surgical wards in seven Belgian hospitals. All patients admitted to these wards during the control period were included in this study. Pregnant patients or children below 17 years of age were excluded. In all patients, we collected age, crude ward mortality, unexpected death, cardiac arrest with Cardiopulmonary Resuscitation (CPR), and unplanned admission to the Intensive Care Unit (ICU). A composite mortality measure was constructed including unexpected death and death up to 72 h after cardiac arrest with CPR or unplanned ICU admission. Every 4 months we obtained, from 30 consecutive patient admissions across all wards, the Charlson comorbidity index. The amount of nursing hours per patient days (NHPPD) were calculated every day for 15 days, once every 4 months. Data were aggregated to the ward level resulting in 68 estimates across wards and time. Linear mixed models were used since they are most appropriate in case of clustered and repeated measures data. Results The unexpected death rate was 1.80 per 1000 patients. Up to 0.76 per 1000 patients died after CPR and 0.62 per 1000 patients died after unplanned admission to the ICU. The mean composite mortality was 3.18 per 1000 patients. The mean NHPPD and proportion of nurse Bachelor hours were respectively 2.48 and 0.59. We found a negative association between the nursing hours per patient day and the composite mortality rate adjusted for possible confounders (B = − 2.771, p  = 0.002). The proportion of nurse Bachelor hours was negatively correlated with the composite mortality rate in the same analysis (B = − 8.845, p  = 0.023). Using the regression equation, we calculated theoretically optimal NHPPDs. Conclusions This study confirms the association between higher nurse staffing levels and lower patient mortality controlled for relevant confounders.

Background Elevated blood pressure (BP) after spontaneous intracerebral hemorrhage (ICH) is associated with poor outcomes, but the prognostic value of prehospital BP, particularly in relation to onset‐to‐arrival time, remains unclear. We investigated time‐dependent associations of prehospital BP, arrival BP, and their early difference with in‐hospital outcomes. Methods We conducted a retrospective cohort study using a prospectively maintained stroke registry at a tertiary medical center in Taipei, Taiwan (2016–2022). Adults (≥ 18 years) with spontaneous ICH transported by emergency medical services within 24 h of symptom onset and with available prehospital BP were included. First prehospital and hospital‐arrival BP were analyzed. Outcomes were in‐hospital mortality and stroke in evolution (SIE) within 72 h of hospital admission. Patients were stratified by onset‐to‐arrival time (< 3 vs. ≥ 3 h). Multivariable logistic regression and restricted cubic splines were applied. Results Six hundred ninety patients were included (mean age 64.7 years, 63.1% male; 336 < 3 h, 354 ≥ 3 h). In patients arriving ≥ 3 h, higher prehospital systolic BP, mean arterial pressure, and pulse pressure were independently associated with increased odds of in‐hospital mortality, and higher arrival pulse pressure was also associated with mortality. Associations with SIE and early BP differences were weaker and were attenuated in fully adjusted models. Restricted cubic spline showed no nonlinear associations. No significant associations between BP measures and outcomes were observed in patients arriving < 3 h. Conclusions Higher prehospital BP was associated with increased in‐hospital mortality among patients presenting ≥ 3 h after onset, whereas such associations are not evident within 3 h. This study, involving 690 adult patients with spontaneous intracerebral hemorrhage, showed that higher prehospital systolic blood pressure, mean arterial pressure, and pulse pressure were independently associated with in‐hospital mortality. In addition, higher pulse pressure on arrival was also associated with mortality. However, these associations were not observed in patients who arrived within 3 h.

Background Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs) make optimal clinical decisions. This study aimed to develop machine learning-based tools to predict the risk of hospital death of patients with sepsis in ICUs. Methods The source database used for model development and validation is the medical information mart for intensive care (MIMIC) III. We identified adult sepsis patients using the new sepsis definition Sepsis-3. A total of 86 predictor variables consisting of demographics, laboratory tests and comorbidities were used. We employed the least absolute shrinkage and selection operator (LASSO), random forest (RF), gradient boosting machine (GBM) and the traditional logistic regression (LR) method to develop prediction models. In addition, the prediction performance of the four developed models was evaluated and compared with that of an existent scoring tool – simplified acute physiology score (SAPS) II – using five different performance measures: the area under the receiver operating characteristic curve (AUROC), Brier score, sensitivity, specificity and calibration plot. Results The records of 16,688 sepsis patients in MIMIC III were used for model training and test. Amongst them, 2949 (17.7%) patients had in-hospital death. The average AUROCs of the LASSO, RF, GBM, LR and SAPS II models were 0.829, 0.829, 0.845, 0.833 and 0.77, respectively. The Brier scores of the LASSO, RF, GBM, LR and SAPS II models were 0.108, 0.109, 0.104, 0.107 and 0.146, respectively. The calibration plots showed that the GBM, LASSO and LR models had good calibration; the RF model underestimated high-risk patients; and SAPS II had the poorest calibration. Conclusion The machine learning-based models developed in this study had good prediction performance. Amongst them, the GBM model showed the best performance in predicting the risk of in-hospital death. It has the potential to assist physicians in the ICU to perform appropriate clinical interventions for critically ill sepsis patients and thus may help improve the prognoses of sepsis patients in the ICU.