Eosinopenia as a predictor of unexpected re-admission and mortality after intensive care unit discharge

Biological age as a predictor of unplanned intensive care readmission during the same hospitalization

BACKGROUND

Biological age is increasingly being recognized as an important predictor of health but its utility in acute care setting remains uncertain.

OBJECTIVE

We assessed whether biological age on intensive care unit (ICU) admission can predict unplanned ICU readmission during the same hospitalization.

METHODS

The Levine PhenoAge model based on biomarkers of DNA methylation was used to determine each patient's biological age. The difference between PhenoAge and chronological age was indexed to the local context by regressing PhenoAge on chronological age using linear regression. A positive residual implied one's biological age was older than the corresponding chronological age compared to other patients - defined as PhenoAgeAccel.

RESULTS

Of the 2950 patients included, 153 (5.2%) had unplanned ICU readmission. Chronological age, Acute Physiology and Chronic Health Evaluation II score, the use of mechanical ventilation, vasopressor, or renal replacement therapy were not significantly different between those with and without readmission. PhenoAgeAccel was, however, more common among those who had unplanned ICU readmission (52% vs 43%, p =0.031). Quantitatively, the degree of phenotypical age above chronological age exhibited a 'dose-related' relationship with the risk of readmission (odds ratio 1.12, 95% confidence interval 1.01-1.24; p=0.040) after adjusting for chronological age, comorbidities, and severity of acute illness in the index (first) ICU admission.

CONCLUSION

Biological age was predictive of unplanned ICU readmission during the same hospitalization.

Predictive Modeling for Readmission to Intensive Care: A Systematic Review

To evaluate the methodologic rigor and predictive performance of models predicting ICU readmission; to understand the characteristics of ideal prediction models; and to elucidate relationships between appropriate triage decisions and patient outcomes.

DATA SOURCES

PubMed, Web of Science, Cochrane, and Embase.

STUDY SELECTION

Primary literature that reported the development or validation of ICU readmission prediction models within from 2010 to 2021.

DATA EXTRACTION

Relevant study information was extracted independently by two authors using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool. Data sources, modeling methodology, definition of outcomes, performance, and risk of bias were critically evaluated to elucidate relevant relationships.

DATA SYNTHESIS

Thirty-three articles describing models were included. Six studies had a high overall risk of bias due to improper inclusion criteria or omission of critical analysis details. Four other studies had an unclear overall risk of bias due to lack of detail describing the analysis. Overall, the most common (50% of studies) source of bias was the filtering of candidate predictors via univariate analysis. The poorest performing models used existing clinical risk or acuity scores such as Acute Physiologic Assessment and Chronic Health Evaluation II, Sequential Organ Failure Assessment, or Stability and Workload Index for Transfer as the sole predictor. The higher-performing ICU readmission prediction models used homogenous patient populations, specifically defined outcomes, and routinely collected predictors that were analyzed over time.

CONCLUSIONS

Models predicting ICU readmission can achieve performance advantages by using longitudinal time series modeling, homogenous patient populations, and predictor variables tailored to those populations.

Evaluation of Eosinopenia as a SIRS Biomarker in Critically Ill Horses

Systemic inflammatory response syndrome (SIRS) is a very common finding in critically ill patients. To accurately identify patients with SIRS and those who need intensive care, several markers have been evaluated, including cortisol, WBC or lactate. It is widely known that a stress leukogram includes eosinopenia as one of its main markers (neutrophilia, eosinopenia, lymphopenia and mild monocytes). It is known that cortisol concentration in plasma is the main stress biomarker and is strongly correlated with the severity of disease in horses. However, it is not possible to measure this parameter routinely in clinical conditions. Hence, in this study it was hypothesized that the eosinophil count could be a reliable parameter to identify critically ill horses. Horses included in this study were divided into three groups: Group A (sick horses received at the Emergency Unit which did not fulfil the criteria for SIRS), Group B (horses that meet two or more criteria for inclusion in the definition of SIRS) and a control group of healthy horses. In this study, horses with SIRS showed lower eosinophil counts than healthy horses. Moreover, non-surviving horses exhibited lower eosinophil counts than survivors. Eosinopenia could be used to identify horses with SIRS and can be useful as a prognostic marker.

Eosinopenia Predicting Long-term Mortality in Hospitalized Acute Exacerbation of COPD Patients with Community-acquired Pneumonia-A Retrospective Analysis

Background

Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) could be triggered by community-acquired pneumonia (CAP). Peripheral blood eosinopenia are strongly associated with increased mortality. In hospitalized AECOPD patients with CAP, eosinopenia may be used to identify patients with high risk of death on admission.

Methods

We conducted a retrospective cohort study in 82 hospitalized AECOPD patients with CAP. Patients who had received systemic corticosteroids preadmission were excluded. The patients were identified by individual case file review. According to blood eosinophil count, patients were grouped as eosinopenia (<50/μL) and non-eosinopenia (≥50/μL). Association of eosinopenia with infection and 18-month survival were analyzed using appropriate statistical methods.

Results

Baseline demographic, comorbidity, CURB65 and Pneumonia Severity Index scores were similar between two groups. The eosinopenia group had significantly higher pro-brain natriuretic peptide, D-dimer, neutrophil percentage, and lower lymphocyte count and lymphocyte percentage. The eosinopenia group had significantly higher C-reactive protein (median 77.30 vs 16.55, p=0.008) and procalcitonin (median 0.32 vs 0.12, p=0.001). Survival at 18 months after hospital discharge was significantly lower in the eosinopenia group vs non-eosinopenia group (log rank, p=0.002).

Conclusion

Eosinopenia (<50/μL) was a strong predictor of 18-month mortality and associated with more severe infection in hospitalized AECOPD patients with CAP. Eosinophil count at admission can be used as a prognosis marker of mortality in those population.

The role of eosinophils in sepsis and acute respiratory distress syndrome: a scoping review

PURPOSE

Septic shock and acute respiratory distress syndrome (ARDS) are characterized by a dysregulated immune host response that may respond to steroid therapy. Eosinophils contribute to type 2 inflammation that often responds to steroid therapy; their role in immune dysregulation and outcomes in sepsis and ARDS is unclear.

SOURCE

A systematic search of Cochrane Library, MEDLINE, and EMBASE was performed from inception to 9 September 2020. The search comprised the following terms: eosinophils, sepsis, septic shock, and ARDS. Two reviewers independently screened abstracts and texts and extracted data on disease severity and clinical outcomes.

PRINCIPAL FINDINGS

Thirty-nine studies were identified: 30 evaluated serum eosinophil count in sepsis, one evaluated eosinophil activity in sepsis, three assessed bronchoalveolar lavage (BAL) eosinophil count in ARDS, four assessed eosinophil activity in ARDS, and one assessed peripheral eosinophil count in ARDS. Eleven studies showed an association between eosinopenia and sepsis, and eight studies found persistent eosinopenia at > 48 hr of intensive care unit admission to predict mortality and readmission in septic patients. Three studies found BAL eosinophil count to be low in ARDS, although one found that levels rose in late-phase ARDS. Three studies found eosinophil activity markers in BAL to be high in ARDS and correlate with ARDS severity.

CONCLUSION

Persistent peripheral eosinopenia is a marker of bacterial sepsis and is independently associated with poor outcomes. Bronchoalveolar lavage eosinophil counts are low in early-phase ARDS, but increase in late-phase ARDS, while elevated markers of eosinophil activity correlate with ARDS severity. Further studies understanding the mechanisms leading to eosinopenia in sepsis and increased eosinophil activity in ARDS are needed.

Eosinopenia and neutrophil-to-lymphocyte count ratio as prognostic factors in exacerbation of COPD

Exacerbations of Chronic Obstructive Pulmonary Disease (AECOPDs) are one of the most important clinical aspects of the disease, and when requiring hospital admission, they significantly contribute to mortality among COPD patients. Our aim was to assess the role of eosinopenia and neutrophil-to-lymphocyte count (NLR) as markers of in-hospital mortality and length of hospitalization (LoH) among patients with ECOPD requiring hospitalization. We included 275 patients. Eosinopenia was associated with in-hospital deaths only when coexisted with lymphocytopenia, with the specificity of 84.4% (95% CI 79.6-88.6%) and the sensitivity of 100% (95% CI 35.9-100%). Also, survivors presented longer LoH (P < 0.0001). NLR ≥ 13.2 predicted in-hospital death with the sensitivity of 100% (95% CI 35.9-100%) and specificity of 92.6% (95% CI 88.8-95.4%), however, comparison of LoH among survivors did not reach statistical significance (P = 0.05). Additionally, when we assessed the presence of coexistence of eosinopenia and lymphocytopenia first, and then apply NLR, sensitivity and specificity in prediction of in-hospital death was 100% (95% CI 35.9-100) and 93.7% (95% CI 90.1-96.3), respectively. Moreover, among survivors, the occurrence of such pattern was associated with significantly longer LoH: 11 (7-14) vs 7 (5-10) days (P = 0.01). The best profile of sensitivity and specificity in the prediction of in-hospital mortality in ECOPD can be obtained by combined analysis of coexistence of eosinopenia and lymphocytopenia with elevated NLR. The occurrence of a such pattern is also associated with significantly longer LoH among survivors.

Do Blood Eosinophils Predict in-Hospital Mortality or Severity of Disease in SARS-CoV-2 Infection? A Retrospective Multicenter Study

Introduction: Healthcare systems worldwide have been battling the ongoing COVID-19 pandemic. Eosinophils are multifunctional leukocytes implicated in the pathogenesis of several inflammatory processes including viral infections. We focus our study on the prognostic value of eosinopenia as a marker of disease severity and mortality in COVID-19 patients. Methods: Between 1 March and 30 April 2020, we conducted a multicenter and retrospective study on a cohort of COVID-19 patients (moderate or severe disease) who were hospitalized after presenting to the emergency department (ED). We led our study in six major hospitals of northeast France, one of the outbreak’s epicenters in Europe. Results: We have collected data from 1035 patients, with a confirmed diagnosis of COVID-19. More than three quarters of them (76.2%) presented a moderate form of the disease, while the remaining quarter (23.8%) presented a severe form requiring admission to the intensive care unit (ICU). Mean circulating eosinophils rate, at admission, varied according to disease severity (p < 0.001), yet it did not differ between survivors and non-survivors (p = 0.306). Extreme eosinopenia (=0/mm³) was predictive of severity (aOR = 1.77, p = 0.009); however, it was not predictive of mortality (aOR = 0.892, p = 0.696). The areas under the Receiver operating characteristics (ROC) curve were, respectively, 58.5% (CI95%: 55.3–61.7%) and 51.4% (CI95%: 46.8–56.1%) for the ability of circulating eosinophil rates to predict disease severity and mortality. Conclusion: Eosinopenia is very common and often profound in cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Eosinopenia was not a useful predictor of mortality; however, undetectable eosinophils (=0/mm³) were predictive of disease severity during the initial ED management.

After-Hours/Nighttime Transfers Out of the Intensive Care Unit and Patient Outcomes: A Systematic Review and Meta-Analysis

PURPOSE

We evaluated the effects of after-hours/nighttime patient transfers out of the ICU on patient outcomes, by performing a systematic review and meta-analysis (PROSPERO CRD 42017074082).

DATA SOURCES

MEDLINE, PubMed, EMBASE, Google Scholar, CINAHL, and the Cochrane Library from 1987-November 2019. Conference abstracts from the Society of Critical Care Medicine, American Thoracic Society, CHEST, Critical Care Canada Forum, and European Society of Intensive Care Medicine from 2011-2019.

DATA EXTRACTION

Observational or randomized studies of adult ICU patients were selected if they compared after-hours transfer out of the ICU to daytime transfer on patient outcomes. Case reports, case series, letters, and reviews were excluded. Study year, country, design, co-variates for adjustment, definitions of after-hours, mortality rates, ICU readmission rates, and hospital length of stay (LOS) were extracted.

DATA SYNTHESIS

We identified 3,398 studies. Thirty-one observational studies (1,418,924 patients) were selected for the systematic review and meta-analysis. Included studies had varying definitions of after-hours, with the after-hours period starting anytime between 16:00-22:00 and ending between 06:00-09:00. Approximately 16% of transfers occurred after-hours. After-hours transfers were associated with increased in-hospital mortality for both unadjusted (odds ratio [OR] 1.51, 95% confidence interval [CI] 1.30-1.75, I² = 96%, number of studies [n] = 26, P < 0.001, low certainty) and adjusted (OR 1.32, 95% CI 1.25-1.38, I ² = 33%, n = 10, P < 0.001, low certainty) data, compared to daytime transfers. They were also associated with increased ICU readmission (pooled unadjusted OR 1.28, 95% CI 1.18-1.38, I² = 85%, n = 17, P < 0.001, low certainty) and longer hospital LOS (standardized mean difference 0.13, 95% CI 0.09-0.18, I ² = 93%, n = 9, P < 0.001, low certainty), compared to daytime transfers.

CONCLUSIONS

After-hours transfers out of the ICU are associated with increased in-hospital mortality, ICU readmission, and hospital LOS, across many settings. While the certainty of evidence is low, future research is needed to reduce the number and effects of after-hours transfers.

Eosinophil count and mortality risk in incident hemodialysis patients

BACKGROUND

Eosinophils are traditionally known as moderators of allergic reactions; however, they have now emerged as one of the principal immune-regulating cells as well as predictors of vascular disease and mortality in the general population. Although eosinophilia has been demonstrated in hemodialysis (HD) patients, associations of eosinophil count (EOC) and its changes with mortality in HD patients are still unknown.

METHODS

In 107 506 incident HD patients treated by a large dialysis organization during 2007-11, we examined the relationships of baseline and time-varying EOC and its changes (ΔEOC) over the first 3 months with all-cause mortality using Cox proportional hazards models with three levels of hierarchical adjustment.

RESULTS

Baseline median EOC was 231 (interquartile range 155-339) cells/μL and eosinophilia (>350 cells/μL) was observed in 23.4% of patients. There was a gradual increase in EOC over time after HD initiation with a median ΔEOC of 5.1 (IQR -53-199) cells/μL, which did not parallel the changes in white blood cell count. In fully adjusted models, mortality risk was highest in subjects with lower baseline and time-varying EOC (<100 cells/μL) and was also slightly higher in patients with higher levels (≥550 cells/μL), resulting in a reverse J-shaped relationship. The relationship of ΔEOC with all-cause mortality risk was also a reverse J-shape where both an increase and decrease exhibited a higher mortality risk.

CONCLUSIONS

Both lower and higher EOCs and changes in EOC over the first 3 months after HD initiation were associated with higher all-cause mortality in incident HD patients.

Absolute Eosinophil Count Predicts Intensive Care Unit Transfer Among Elderly COVID-19 Patients From General Isolation Wards

Objectives: As of June 1, 2020, coronavirus disease 2019 (COVID-19) has caused a global pandemic and resulted in over 370,000 deaths worldwide. Early identification of COVID-19 patients who need to be admitted to the intensive care unit (ICU) helps to improve the outcomes. We aim to investigate whether absolute eosinophil count (AEC) can predict ICU transfer among elderly COVID-19 patients from general isolation wards. Methods: A retrospective study of 94 elderly patients older than 60 years old with COVID-19 was conducted. We compared the basic clinical characteristics and levels of inflammation markers on admission to general isolation wards and the needs for ICU transfer between the eosinopenia (AEC on admission <20 cells/μl) and non-eosinopenia (AEC ≥20 cells/μl) groups. Results: There was a significantly higher ICU transfer rate in the eosinopenia group than in the non-eosinopenia group (51 vs. 9%, P < 0.001). Multivariate analysis revealed that eosinopenia was associated with an increased risk of ICU transfer in elderly COVID-19 patients [adjusted odds ratio (OR) 6.12 (95% CI, 1.23-30.33), P = 0.027] after adjustment of age, lymphocyte count, neutrophil count, C-reactive protein (CRP), and ferritin levels. The eosinopenia group had higher levels of CRP, ferritin, and cytokines [interleukin-2 receptor (IL-2R), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-10 (IL-10), and tumor necrosis factor-α (TNF-α)] than the non-eosinophil group (P < 0.001). The area under the curve of AEC on admission for predicting ICU transfer among elderly COVID-19 patients was 0.828 (95% CI, 0.732-0.923). The best cut-off value of AEC was 25 cells/μl with a sensitivity of 91% and a specificity of 71%, respectively. Conclusion: Absolute eosinophil count on admission is a valid predictive marker for ICU transfer among elderly COVID-19 patients from general isolation wards and, therefore, can help case triage and optimize ICU utilization, especially for health care facilities with limited ICU capacity.

White blood cell count and eosinopenia as valuable tools for the diagnosis of bacterial infections in the ED

Identifying an infection may be difficult in the ED. Neutrophilic leukocytosis is often used in the diagnosis of infection despite its lack of specificity in situations of stress. Our objective was to study the value of each parameter of the WBC count, in particular eosinopenia, to diagnose bacterial infections in the ED. We conducted a retrospective and observational study over a period of 6 months. All patients with one of the following diagnoses were eligible: pneumonia (9.9%), pyelonephritis (26.2%), prostatitis (8.4%), appendicitis (26.2%), cholecystitis (8.4%), and diverticular sigmoiditis (5%). A total of 466 infected patients were included for statistical analysis, and a control group of 466 uninfected patients was randomly selected in the same period of time. All leukocyte count parameters were significantly modified (p < 0.001) in the infected group compared with the control group. Neutrophils and total leukocytes remain the two most suitable parameters for the diagnosis of infections in the ED. Eosinopenia represented the most efficient parameter of the WBC count for the diagnosis of urinary and biliary tract infections. Deep eosinopenia presented a specificity of 94% for the diagnosis of infection. Any modification of the WBC count associated with an elevation of CRP (> 40 mg/L) or fever (> 38.5 °C) showed a high specificity for the diagnosis of infection. A careful analysis of the WBC count remains a valuable tool for the diagnosis of infection in the ED.

Out-of-hours discharge from intensive care, in-hospital mortality and intensive care readmission rates: a systematic review and meta-analysis

PURPOSE

Discharge from an intensive care unit (ICU) out of hours is common. We undertook a systematic review and meta-analysis to explore the association between time of discharge and mortality/ICU readmission.

METHODS

We searched Medline, Embase, Web of Knowledge, CINAHL, the Cochrane Library and OpenGrey to June 2017. We included studies reporting in-hospital mortality and/or ICU readmission rates by ICU discharge "out-of-hours" and "in-hours". Inclusion was limited to patients aged ≥ 16 years discharged alive from a non-specialist ICU to a lower level of hospital care. Studies restricted to specific diseases were excluded. We assessed study quality using the Newcastle Ottowa Scale. We extracted published data, summarising using a random-effects meta-analysis.

RESULTS

Our searches identified 1961 studies. We included unadjusted data from 1,191,178 patients from 18 cohort studies (presenting data from 1994 to 2014). "Out of hours" had multiple definitions, beginning between 16:00 and 22:00 and ending between 05:59 and 09:00. Patients discharged out of hours had higher in-hospital mortality [relative risk (95% CI) 1.39 (1.24, 1.57) p < 0.0001] and readmission rates [1·30 (1.19, 1.42), p < 0.001] than patients discharged in hours. Heterogeneity was high (I2 90.1% for mortality and 90.2% for readmission), resulting from differences in effect size rather than the presence of an effect.

CONCLUSIONS

Out-of-hours discharge from an ICU is strongly associated with both in-hospital death and ICU readmission. These effects persisted across all definitions of "out of hours" and across healthcare systems in different geographical locations. Whether these increases in mortality and readmission result from patient differences, differences in care, or a combination remains unclear.

Pitfalls in studies of eosinopenia and neutrophil-to-lymphocyte count ratio

There is a number of publications evaluating the eosinophil count and the neutrophil-to-lymphocyte count ratio for diagnosis, prognosis or monitoring of patients. Of special interest is the use of these parameters for discrimination between the different causes of fever (e.g. bacterial versus viral vs. non-infectious causes of fever) and for monitoring the efficacy of therapy and predict the course of the patient. However, pitfalls in previous study designs prevent applicability to clinical practice. Here, we provide a short review of the relevant literature and summarize important factors that should be taken into account when designing studies, with special attention to the selection of a proper and clinically meaningful study population and the effects of the stress response and of corticosteroids.

Effect of non-linearity of a predictor on the shape and magnitude of its receiver-operating-characteristic curve in predicting a binary outcome

Area under a receiver-operating-characteristic (AUROC) curve is widely used in medicine to summarize the ability of a continuous predictive marker to predict a binary outcome. This study illustrated how a U-shaped or inverted U-shaped continuous predictor would affect the shape and magnitude of its AUROC curve in predicting a binary outcome by comparing the ROC curves of the worst first 24-hour arterial pH values of 9549 consecutive critically ill patients in predicting hospital mortality before and after centering the predictor by its mean or median. A simulation dataset with an inverted U-shaped predictor was used to assess how this would affect the shape and magnitude of the AUROC curve. An asymmetrical U-shaped relationship between pH and hospital mortality, resulting in an inverse-sigmoidal ROC curve, was observed. The AUROC substantially increased after centering the predictor by its mean (0.611 vs 0.722, difference = 0.111, 95% confidence interval [CI] 0.087–0.135), and was further improved after centering by its median (0.611 vs 0.745, difference = 0.133, 95%CI 0.110–0.157). A sigmoidal-shaped ROC curve was observed for an inverted U-shaped predictor. In summary, a non-linear predictor can result in a biphasic-shaped ROC curve; and centering the predictor can reduce its bias towards null predictive ability.

Readmission of ICU patients: A quality indicator?

PURPOSE

Readmission rate is frequently proposed as a quality indicator because it is related to both patient outcome and organizational efficiency. Currently available studies are not clear about modifiable factors as tools to reduce readmission rate.

MATERIAL AND METHODS

In a 14year retrospective cohort study of 19,750 ICU admissions we identified 1378 readmissions (7%). A multivariate logistic regression analysis for determinants of readmission within 24h, 48h, 72h and any time during hospital admission was performed with adjustment for patients' characteristics and initial admission severity scores.

RESULTS

In all models with different time points, patients with older age, a medical and emergency surgery initial admission and patients with higher SOFA score have a higher risk of readmission. Immunodeficiency was a predictor only in the at any time model. Confirmed infection was predicted in all models except the 24h model. Last day noradrenaline treatment was predicted in the 24 and 48h model. Mechanical ventilation on admission independently protected for readmission, which can be explained by the large number of cardiac surgery patients. All multivariate models had a moderate performance with the highest AUC of 0.70.

CONCLUSIONS

Readmission can be predicted with moderate precision and independent variables associated with readmission are age, severity of disease, type of admission, infection, immunodeficiency and last day noradrenaline use. The latter factor is the only one that can be modified and therefore readmission rate does not meet the criteria to be used as a useful quality indicator.

Association of severity of illness and intensive care unit readmission: A systematic review

OBJECTIVES

To determine whether ICU readmission is associated with higher severity of illness scores in adult patients.

BACKGROUND

Readmissions to the intensive care unit (ICU) are associated with increased costs, morbidity, and mortality.

METHODS

We performed searches of MEDLINE, EMBASE, and grey literature databases. We selected studies reporting data from adults who were hospitalized in an ICU, received severity of illness scores, and were discharged from the ICU. Characteristics of readmitted and non-readmitted patients were examined.

RESULTS

We screened 4766 publications and included 31 studies in our analysis. In most studies, severity of illness scores were higher in patients readmitted to the ICU. Readmission was also associated with higher mortality and longer ICU and hospital stays. Excessive heterogeneity precluded the reporting of results in the form of a meta-analysis.

CONCLUSIONS

ICU readmission is associated with higher severity of illness scores during the same hospitalization in adult patients.

Procalcitonin concentrations as a predictor of unexpected readmission and mortality after intensive care unit discharge: A retrospective cohort study

Procalcitonin (PCT) has been used to guide treatment in critically ill patients with sepsis, but whether PCT at intensive care unit (ICU) discharge can stratify risks of post-ICU readmission or mortality is unknown. This cohort study compared the ability of PCT with C-reactive protein (CRP) in predicting unexpected adverse post-ICU events. Of the 1877 patients admitted to the multidisciplinary ICU between 1 April 2012 and 31 March 2014, 1653 (88.1%) were discharged without treatment limitations. A total of 71 (4.3%) were readmitted and 18 patients (1%) died unexpectedly after ICU discharge during the same hospitalization. Both PCT (0.6 vs 0.4 μg/L, P = .002) and a high CRP concentration >100 mg/L (58% vs 41%, P = .004) at ICU discharge were associated with an increased risk of adverse post-ICU events in the univariate analyses; however, the ability of PCT to discriminate between patients with and without adverse post-ICU outcomes was limited (area under the receiver operating characteristic curve = 0.61; 95% confidence interval, 0.55-0.66). In the multivariable analysis, only a high CRP concentration (odds ratio, 1.92; 95% confidence interval, 1.12-3.11; P = .008) was associated with an increased adverse post-ICU events. Elevated PCT concentration at ICU discharge was inadequate in its predictive ability to guide ICU discharge.

Eosinophil count at intensive care unit admission was not predictor of hospital mortality: results of a case control study

Background

Predicting mortality in the intensive care unit (ICU) is one of the biggest challenges in critical care medicine. Several studies have linked the presence of eosinopenia with adverse outcomes in different populations.

Methods

We performed a case control study to determine whether the eosinophil count at ICU admission was a predictor of hospital mortality. We included data from patients 18 years or older admitted to the medical or surgical ICU in a university hospital in northern of Mexico. Medical records of 86 non-survivors (cases) and 99 discharged alive patients (controls) were randomly reviewed; clinical records of patients with an ICU stay of less than 24 h and those whose information was incomplete were excluded.

Results

Median of eosinophil count at ICU admission was 0.013 (interquartile range (IQR) 0.00 to 0.57) K/μL. There was no significant statistical difference in eosinophils at admission between survivors and non-survivors (0.014 [IQR 0.00 to 0.36] vs. 0.010 [IQR 0.00 to 0.57] K/μL, P = 0.35). In the multivariate analysis, APACHE II score at ICU admission and discharge were the only mortality predictors. Survivors had a significantly greater increase in eosinophil count during the first 7 days of ICU stay (0.104 [IQR −0.64 to 0.41] vs. 0.005 [IQR −1.79 to 0.43] K/μL, P = 0.004).

Conclusions

In our study, eosinophil count at ICU admission was not associated with increased hospital mortality. The larger increase in number of eosinophils observed during the first week of ICU stay in surviving patients deserves to be investigated further.

Efficacy of the APACHE II score at ICU discharge in predicting post-ICU mortality and ICU readmission in critically ill surgical patients

In this study, we evaluated the efficacy of the discharge Acute Physiology and Chronic Health Evaluation (APACHE) II score in predicting post-intensive care unit (ICU) mortality and ICU readmission during the same hospitalisation in a surgical ICU. Of 1190 patients who were admitted to the ICU and stayed >48 hours between October 2007 and March 2010, 23 (1.9%) died and 86 (7.2%) were readmitted after initial ICU discharge, with 26 (3.0%) admitted within 48 hours. The area under the receiver operating characteristics curve of the discharge and admission APACHE II scores in predicting in-hospital mortality was 0.631 (95% confidence interval [CI] 0.603 to 0.658) and 0.669 (95% CI 0.642 to 0.696), respectively (P=0.510). The area under the receiver operating characteristics curve of discharge and admission APACHE II scores for predicting all forms of readmission was 0.606 (95% CI 0.578 to 0.634) and 0.574 (95% CI 0.545 to 0.602), respectively (P=0.316). The area under the receiver operating characteristics curve of discharge APACHE II score in predicting early ICU readmissions was, however, higher than that of admission APACHE II score (0.688 [95% CI 0.660 to 0.714] versus 0.505 [95% CI 0.476 to 0.534], P=0.001). The discharge APACHE II score (odds ratio [OR] 1.1, 95% CI 1.01 to 1.22, P=0.024), unplanned ICU readmission (OR 20.0, 95% CI 7.6 to 53.1, P=0.001), eosinopenia at ICU discharge (OR 6.0, 95% CI 1.34 to 26.9, P=0.019), and hospital length-of-stay before ICU admission (OR 1.02, 95% CI 1.01 to 1.03, P=0.021) were significant independent factors in predicting post-ICU mortality. This study suggests that the discharge APACHE II score may be useful in predicting post-ICU mortality and is superior to the admission APACHE II score in predicting early ICU readmission in surgical ICU patients.

A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care

Introduction

We sought to derive literature-based summary estimates of readmission to the ICU and hospital mortality among patients discharged alive from the ICU.

Methods

We searched MEDLINE, Embase, CINAHL and the Cochrane Central Register of Controlled Trials from inception to March 2013, as well as the reference lists in the publications of the included studies. We selected cohort studies of ICU discharge prognostic factors that in which readmission to the ICU or hospital mortality among patients discharged alive from the ICU was reported. Two reviewers independently abstracted the number of patients readmitted to the ICU and hospital deaths among patients discharged alive from the ICU. Fixed effects and random effects models were used to estimate the pooled cumulative incidence of ICU readmission and the pooled cumulative incidence of hospital mortality.

Results

The analysis included 58 studies (n = 2,073,170 patients). The majority of studies followed patients until hospital discharge (n = 46 studies) and reported readmission to the ICU (n = 46 studies) or hospital mortality (n = 49 studies). The cumulative incidence of ICU readmission was 4.0 readmissions (95% confidence interval (CI), 3.9 to 4.0) per 100 patient discharges using fixed effects pooling and 6.3 readmissions (95% CI, 5.6 to 6.9) per 100 patient discharges using random effects pooling. The cumulative incidence of hospital mortality was 3.3 deaths (95% CI, 3.3 to 3.3) per 100 patient discharges using fixed effects pooling and 6.8 deaths (95% CI, 6.1 to 7.6) per 100 patient discharges using random effects pooling. There was significant heterogeneity for the pooled estimates, which was partially explained by patient, institution and study methodological characteristics.

Conclusions

Using current literature estimates, for every 100 patients discharged alive from the ICU, between 4 and 6 patients on average will be readmitted to the ICU and between 3 and 7 patients on average will die prior to hospital discharge. These estimates can inform the selection of benchmarks for quality metrics of transitions of patient care between the ICU and the hospital ward.