Effect of heart rate on hospital mortality in critically ill patients may be modified by age: a retrospective observational study from large database

The search for optimal nocturnal diurnal heart rate Index targets in ICU patients: a retrospective observational study from large ICU database

Background

Circadian rhythms play a crucial role in cardiovascular health, with the nocturnal diurnal heart rate index (NDHRI) reflecting significant circadian variations. However, the optimal NDHRI target in Intensive Care Unit (ICU) patients remains undefined. This study aims to establish an evidence-based NDHRI target range and assess its association with mortality.

Methods

Data from the eICU Collaborative Research Database (n = 32,412) were analyzed. NDHRI was calculated by dividing cumulative nighttime heart rate area by daytime area. Generalized additive models (GAMs) explored the non-linear relationship between mean NDHRI and mortality, adjusting for confounders. Subgroup analyses were conducted based on ethnicity, ICU type, and comorbidities.

Results

A U-shaped association was observed between hospital mortality and mean NDHRI (P < 0.001). The optimal NDHRI range (40.0%-45.0%) demonstrated the lowest mortality rates. The duration spent within this range correlated inversely with mortality (P < 0.001). Subgroup analyses consistently supported these findings across diverse patient profiles.

Conclusions

Our findings suggest an association between maintaining NDHRI within the 40.0%-45.0% range and lower mortality rates in critically ill patients, highlighting the potential utility of monitoring circadian heart rate variations in the ICU. Further research and future randomized controlled trials are essential to confirm causality and should consider this NDHRI range as a pivotal reference target.

Heart/breathing rate ratio (HBR) as a predictor of mortality in critically ill patients

Objectives

The early prediction of death is a challenge for medical staff. We evaluated the ability of the heart/breathing rate ratio (HBR) to predict mortality.

Methods

This was a single-center retrospective observational study of adult patients who had fever with or without respiratory symptoms, who survived at least 2 h after visiting the hospital, and whose lactate levels and vital signs were tested. We evaluated the distribution of mortality at different HBR levels and compared HBR with lactate.

Results

A total of 18,872 fever clinic visits were screened, and 183 patients whose lactate levels were tested were recruited. Patients who had HBR values lower than 4·5 or higher than 5·5 had greater mortality than patients who had HBR values between 4·5 and 5·5 (21·3 % vs. 3·4 %, p = 0·003; 28·9 % vs. 3·4 %, p < 0·001, respectively). In patients whose HBR was <5, the AUROC for HBR for mortality was 0·762 (95 % CI: 0.643-0·880), and that for lactate was 0·701 (95 % CI: 0·564-0·837). In patients whose HBR was ≥5, the AUROC for HBR for mortality was 0·721 (95 % CI: 0·584-0·857), and that for lactate was 0·742 (95 % CI: 0·607-0·848).

Conclusions

HBR is helpful for stratifying mortality risk among critically ill patients in acute care clinics for infectious diseases.

Identifying vital sign trajectories to predict 28-day mortality of critically ill elderly patients with acute respiratory distress syndrome

BACKGROUND

The mortality rate of acute respiratory distress syndrome (ARDS) increases with age (≥ 65 years old) in critically ill patients, and it is necessary to prevent mortality in elderly patients with ARDS in the intensive care unit (ICU). Among the potential risk factors, dynamic subphenotypes of respiratory rate (RR), heart rate (HR), and respiratory rate-oxygenation (ROX) and their associations with 28-day mortality have not been clearly explored.

METHODS

Based on the eICU Collaborative Research Database (eICU-CRD), this study used a group-based trajectory model to identify longitudinal subphenotypes of RR, HR, and ROX during the first 72 h of ICU stays. A logistic model was used to evaluate the associations of trajectories with 28-day mortality considering the group with the lowest rate of mortality as a reference. Restricted cubic spline was used to quantify linear and nonlinear effects of static RR-related factors during the first 72 h of ICU stays on 28-day mortality. Receiver operating characteristic (ROC) curves were used to assess the prediction models with the Delong test.

RESULTS

A total of 938 critically ill elderly patients with ARDS were involved with five and 5 trajectories of RR and HR, respectively. A total of 204 patients fit 4 ROX trajectories. In the subphenotypes of RR, when compared with group 4, the odds ratios (ORs) and 95% confidence intervals (CIs) of group 3 were 2.74 (1.48-5.07) (P = 0.001). Regarding the HR subphenotypes, in comparison to group 1, the ORs and 95% CIs were 2.20 (1.19-4.08) (P = 0.012) for group 2, 2.70 (1.40-5.23) (P = 0.003) for group 3, 2.16 (1.04-4.49) (P = 0.040) for group 5. Low last ROX had a higher mortality risk (P linear = 0.023, P nonlinear = 0.010). Trajectories of RR and HR improved the predictive ability for 28-day mortality (AUC increased by 2.5%, P = 0.020).

CONCLUSIONS

For RR and HR, longitudinal subphenotypes are risk factors for 28-day mortality and have additional predictive enrichment, whereas the last ROX during the first 72 h of ICU stays is associated with 28-day mortality. These findings indicate that maintaining the health dynamic subphenotypes of RR and HR in the ICU and elevating static ROX after initial critical care may have potentially beneficial effects on prognosis in critically ill elderly patients with ARDS.