Presenting hemodynamic phenotypes in ED patients with confirmed sepsis

Clinical Sepsis Phenotypes in Critically Ill Patients

Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process.

Methods for Phenotyping Adult Patients in Sepsis and Septic Shock: A Scoping Review

Despite its heterogeneous phenotypes, sepsis or life-threatening dysfunction in response to infection is often treated empirically. Identifying patient subgroups with unique pathophysiology and treatment response is critical to the advancement of sepsis care. However, phenotyping methods and results are as heterogeneous as the disease itself. This scoping review evaluates the prognostic capabilities and treatment implications of adult sepsis and septic shock phenotyping methods.

DATA SOURCES

Medline and Embase.

STUDY SELECTION

We included clinical studies that described sepsis or septic shock and used any clustering method to identify sepsis phenotypes. We excluded conference abstracts, literature reviews, comments, letters to the editor, and in vitro studies. We assessed study quality using a validated risk of bias tool for observational cohort and cross-sectional studies.

DATA EXTRACTION

We extracted population, methodology, validation, and phenotyping characteristics from 17 studies.

DATA SYNTHESIS

Sepsis phenotyping methods most frequently grouped patients based on the degree of inflammatory response and coagulopathy using clinical, nongenomic variables. Five articles clustered patients based on genomic or transcriptomic data. Seven articles generated patient subgroups with differential response to sepsis treatments. Cluster clinical characteristics and their associations with mortality and treatment response were heterogeneous across studies, and validity was evaluated in nine of 17 articles, hindering pooled analysis of results and derivation of universal truths regarding sepsis phenotypes, their prognostic capabilities, and their associations with treatment response.

CONCLUSIONS

Sepsis phenotyping methods can identify high-risk patients and those with high probability of responding well to targeted treatments. Research quality was fair, but achieving generalizability and clinical impact of sepsis phenotyping will require external validation and direct comparison with alternative approaches.

Clinical Phenotyping of Out-of-Hospital Cardiac Arrest Patients With Shockable Rhythm - Machine Learning-Based Unsupervised Cluster Analysis

BACKGROUND

The hypothesis of this study is that latent class analysis could identify the subphenotypes of out-of-hospital cardiac arrest (OHCA) patients associated with the outcomes and allow us to explore heterogeneity in the effects of extracorporeal cardiopulmonary resuscitation (ECPR).

METHODS AND RESULTS

This study was a retrospective analysis of a multicenter prospective observational study (CRITICAL study) of OHCA patients. It included adult OHCA patients with initial shockable rhythm. Patients from 2012 to 2016 (development dataset) were included in the latent class analysis, and those from 2017 (validation dataset) were included for evaluation. The association between subphenotypes and outcomes was investigated. Further, the heterogeneity of the association between ECPR implementation and outcomes was explored. In the study results, a total of 920 patients were included for latent class analysis. Three subphenotypes (Groups 1, 2, and 3) were identified, mainly characterized by the distribution of partial pressure of O2(PO2), partial pressure of CO2(PCO2) value of blood gas assessment, cardiac rhythm on hospital arrival, and estimated glomerular filtration rate. The 30-day survival outcomes were varied across the groups: 15.7% in Group 1; 30.7% in Group 2; and 85.9% in Group 3. Further, the association between ECPR and 30-day survival outcomes by subphenotype groups in the development dataset was as varied. These results were validated using the validation dataset.

CONCLUSIONS

The latent class analysis identified 3 subphenotypes with different survival outcomes and potential heterogeneity in the effects of ECPR.

Clustering out‐of‐hospital cardiac arrest patients with non‐shockable rhythm by machine learning latent class analysis

Aim

We aimed to identify subphenotypes among patients with out‐of‐hospital cardiac arrest (OHCA) with initial non‐shockable rhythm by applying machine learning latent class analysis and examining the associations between subphenotypes and neurological outcomes.

Methods

This study was a retrospective analysis within a multi‐institutional prospective observational cohort study of OHCA patients in Osaka, Japan (the CRITICAL study). The data of adult OHCA patients with medical causes and initial non‐shockable rhythm presenting with OHCA between 2012 and 2016 were included in machine learning latent class analysis models, which identified subphenotypes, and patients who presented in 2017 were included in a dataset validating the subphenotypes. We investigated associations between subphenotypes and 30‐day neurological outcomes.

Results

Among the 12,594 patients in the CRITICAL study database, 4,849 were included in the dataset used to classify subphenotypes (median age: 75 years, 60.2% male), and 1,465 were included in the validation dataset (median age: 76 years, 59.0% male). Latent class analysis identified four subphenotypes. Odds ratios and 95% confidence intervals for a favorable 30‐day neurological outcome among patients with these subphenotypes, using group 4 for comparison, were as follows; group 1, 0.01 (0.001–0.046); group 2, 0.097 (0.051–0.171); and group 3, 0.175 (0.073–0.358). Associations between subphenotypes and 30‐day neurological outcomes were validated using the validation dataset.

Conclusion

We identified four subphenotypes of OHCA patients with initial non‐shockable rhythm. These patient subgroups presented with different characteristics associated with 30‐day survival and neurological outcomes.

Cardiovascular Responses During Sepsis

Sepsis is the life-threatening organ dysfunction arising from a dysregulated host response to infection. Although the specific mechanisms leading to organ dysfunction are still debated, impaired tissue oxygenation appears to play a major role, and concomitant hemodynamic alterations are invariably present. The hemodynamic phenotype of affected individuals is highly variable for reasons that have been partially elucidated. Indeed, each patient's circulatory condition is shaped by the complex interplay between the medical history, the volemic status, the interval from disease onset, the pathogen, the site of infection, and the attempted resuscitation. Moreover, the same hemodynamic pattern can be generated by different combinations of various pathophysiological processes, so the presence of a given hemodynamic pattern cannot be directly related to a unique cluster of alterations. Research based on endotoxin administration to healthy volunteers and animal models compensate, to an extent, for the scarcity of clinical studies on the evolution of sepsis hemodynamics. Their results, however, cannot be directly extrapolated to the clinical setting, due to fundamental differences between the septic patient, the healthy volunteer, and the experimental model. Numerous microcirculatory derangements might exist in the septic host, even in the presence of a preserved macrocirculation. This dissociation between the macro- and the microcirculation might account for the limited success of therapeutic interventions targeting typical hemodynamic parameters, such as arterial and cardiac filling pressures, and cardiac output. Finally, physiological studies point to an early contribution of cardiac dysfunction to the septic phenotype, however, our defective diagnostic tools preclude its clinical recognition. © 2021 American Physiological Society. Compr Physiol 11:1605-1652, 2021.

Clinical impact of multi-parameter continuous non-invasive monitoring in hospital wards: a systematic review and meta-analysis

OBJECTIVE

Delayed response to clinical deterioration as a result of intermittent vital sign monitoring is a cause of preventable morbidity and mortality. This review focuses on the clinical impact of multi-parameter continuous non-invasive monitoring of vital signs (CoNiM) in non-intensive care unit patients.

DESIGN

Systematic review and meta-analysis of primary studies. Embase, MEDLINE, HMIC, PsycINFO and Cochrane were searched from April 1964 to 18 June 2019 with no language restriction.

SETTING

The search was limited to hospitalised, non-intensive care unit adult patients who had two or more vital signs continuously monitored.

PARTICIPANTS

All primary studies that evaluated the clinical impact of using multi-parameter CoNiM in adult hospital wards outside of the intensive care unit.

MAIN OUTCOME MEASURES

Clinical impact of multi-parameter CoNiM.

RESULTS

This systematic review identified 14 relevant studies from 3846 search results. Five studies were classified as Group A - associations found between measured vital signs and clinical parameters. Nine studies were classified as Group B - comparison between clinical outcomes of patients with and without multi-parameter CoNiM. Vital signs data from CoNiM were found to associate with type of presenting complaint, level of renal function and incidence of major clinical events. CoNiM also assisted in diagnosis by differentiating between patients with acute heart failure, stroke and sepsis (with sub-clustering of septic patients). In the meta-analysis, patients on multi-parameter CoNiM had a 39% decrease in risk of mortality (risk ratio [RR] 0.61; 95% confidence interval [95% CI] -0.39-0.95) when compared to patients with regular intermittent monitoring. There was a trend of reduced intensive care unit transfer (RR 0.86; 95% CI -0.67-1.11) and reduced rapid response team activation (RR 0.61; 95% CI 0.26-1.43). A trend towards reduced hospital length of stay was also found using weighted mean difference (WMD -3.32 days; 95% CI -8.82-2.19 days).

CONCLUSION

There is evidence of clinical benefit in implementing CoNiM in non-intensive care unit patients. This review supports the use of multi-parameter CoNiM outside of intensive care unit with further large-scale RCTs required to further affirm clinical impact.

Effect of platelet-lymphocyte ratio and lactate levels obtained on mortality with sepsis and septic shock

BACKGROUND

Sepsis is a potentially fatal condition with high treatment costs, and is especially common among the elderly population. The emergency management of septic patients has gained importance.

OBJECTIVE

Herein, we investigated the effect of admission lactate levels and the platelet-lymphocyte ratio (PLR) on the 30-day mortality among patients older than 65years who were diagnosed with sepsis and septic shock according to the qSOFA criteria at our hospital's emergency department.

METHODS

This observational study was conducted retrospectively. We obtained information regarding patients' demographic characteristics, comorbid conditions, hemodynamic parameters at admission, initial treatment needs at the emergency department.

RESULTS

131 patients received a diagnosis of sepsis and septic shock at our emergency department in two years. Among these, 45% (n=59) of the patients died within 30days of admission. Forty (30.5%) patients required mechanical ventilation. There was a significant difference between the survival and non-survival groups with regard to systolic and diastolic blood pressures (p=0.013 and 0.045, respectively). There were significant differences between the two groups with respect to the Glasgow Coma Scale score (p<0.001) and BUN levels (p<0.001). The mortality status according to qSOFA scores was revealed a significant difference between the two groups (p<0.001).

CONCLUSION

Our results showed that the patients who died within 30days of admission and those who did not had comparable PLR and lactate levels (p=0.821 and 0.120, respectively). We opine that serial lactate measurements would be more useful than a single admission lactate measurement for the prediction of mortality.