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Defining persistent critical illness based on growth trajectories in patients with sepsis.

ABSTRACT: BACKGROUND:Persistent critical illness is common in critically ill patients and is associated with vast medical resource use and poor clinical outcomes. This study aimed to define when patients with sepsis would be stabilized and transitioned to persistent critical illness, and whether such transition time varies between latent classes of patients. METHODS:This was a retrospective cohort study involving sepsis patients in the eICU Collaborative Research Database. Persistent critical illness was defined at the time when acute physiological characteristics were no longer more predictive of in-hospital mortality (i.e., vital status at hospital discharge) than antecedent characteristics. Latent growth mixture modeling was used to identify distinct trajectory classes by using Sequential Organ Failure Assessment score measured during intensive care unit stay as the outcome, and persistent critical illness transition time was explored in each latent class. RESULTS:The mortality was 16.7% (3828/22,868) in the study cohort. Acute physiological model was no longer more predictive of in-hospital mortality than antecedent characteristics at 15?days after intensive care unit admission in the overall population. Only a minority of the study subjects (n?=?643, 2.8%) developed persistent critical illness, but they accounted for 19% (15,834/83,125) and 10% (19,975/198,833) of the total intensive care unit and hospital bed-days, respectively. Five latent classes were identified. Classes 1 and 2 showed increasing Sequential Organ Failure Assessment score over time and transition to persistent critical illness occurred at 16 and 27?days, respectively. The remaining classes showed a steady decline in Sequential Organ Failure Assessment scores and the transition to persistent critical illness occurred between 6 and 8?days. Elevated urea-to-creatinine ratio was a good biochemical signature of persistent critical illness. CONCLUSIONS:While persistent critical illness occurred in a minority of patients with sepsis, it consumed vast medical resources. The transition time differs substantially across latent classes, indicating that the allocation of medical resources should be tailored to different classes of patients.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC7029548 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Defining persistent critical illness based on growth trajectories in patients with sepsis.

Zhang Zhongheng Z Ho Kwok M KM Gu Hongqiu H Hong Yucai Y Yu Yunsong Y

Critical care (London, England) 20200218 1

<h4>Background</h4>Persistent critical illness is common in critically ill patients and is associated with vast medical resource use and poor clinical outcomes. This study aimed to define when patients with sepsis would be stabilized and transitioned to persistent critical illness, and whether such transition time varies between latent classes of patients.<h4>Methods</h4>This was a retrospective cohort study involving sepsis patients in the eICU Collaborative Research Database. Persistent critic ...[more]

PMID: 32070393

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Project description:BACKGROUND:Many sepsis survivors develop chronic critical illness (CCI) and are assumed to be immunosuppressed, but there is limited clinical evidence to support this. We sought to determine whether the incidence of secondary infections and immunosuppressive biomarker profiles of patients with CCI differ from those with rapid recovery (RAP) after sepsis. METHODS:This prospective observational study evaluated 88 critically ill patients with sepsis and 20 healthy controls. Cohorts were defined based on clinical trajectory (early death, RAP, or CCI), whereas immunosuppression was clinically determined by the presence of a postsepsis secondary infection. Serial blood samples were collected for absolute lymphocyte counts (ALCs), monocytic human leukocyte antigen-DR (mHLA-DR) expression, and plasma-soluble programmed death-ligand 1 (sPD-L1) concentrations. RESULTS:Of the 88 patients with sepsis, 3 (3%) died within 14 days of sepsis onset, 50 (57%) experienced RAP, and 35 (40%) developed CCI. Compared with RAP patients, CCI patients exhibited a higher incidence and overall number of infections adjusted for hospital length of stay. ALC and mHLA-DR levels were dramatically reduced at the time of sepsis diagnosis when compared with healthy controls, whereas sPD-L1 concentrations were significantly elevated. There were no differences between RAP and CCI patients in ALC, sPD-L1, or mHLA-DR at the time of diagnosis or within 24?h after sepsis diagnosis. However, in contrast to the RAP group, CCI patients failed to exhibit any trend toward restoration of normal values of ALC, HLA-DR, and sPD-L1. CONCLUSIONS:Septic patients demonstrate clinical and biological evidence to suggest they are immunosuppressed at the time of sepsis diagnosis. Those who develop CCI have a greater incidence of secondary infections and persistently aberrant markers of impaired host immunity, although measurements at the time of sepsis onset did not distinguish between subjects with RAP and CCI.

Project description:Background: Sepsis is a heterogenous syndrome with limited therapeutic options. Identifying characteristic gene expression patterns, or endotypes, in septic patients may lead to targeted interventions. We investigated whether patients admitted to a surgical ICU with sepsis and with high risk of mortality express similar endotypes to non-septic, but still critically ill patients using two multiplex transcriptomic metrics obtained both on admission to a surgical intensive care unit (ICU) and at set intervals. Methods: We analyzed transcriptomic data from 522 patients in two single-site, prospective, observational cohorts admitted to surgical ICUs over a 5-year period ending in July 2020 . Using an FDA-cleared analytical platform (nCounter FLEX ® , NanoString, Inc.), we assessed a previously validated 29-messenger RNA transcriptomic classifier for likelihood of 30-day mortality (IMX-SEV-3) and a 33-messenger RNA transcriptomic endotype classifier. Clinical outcomes included all-cause (in-hospital, 30-, 90-day) mortality, development of chronic critical illness (CCI), and secondary infections. Univariate and multivariate analyses were performed to assess for true effect and confounding. Results: Sepsis was associated with a significantly higher predicted and actual hospital mortality. At enrollment, the predominant endotype for both septic and non-septic patients was adaptive , though with significantly different distributions. Inflammopathic and coagulopathic septic patients, as well as inflammopathic non-septic patients, showed significantly higher frequencies of secondary infections compared to those with adaptive endotypes (p<0.01). Endotypes changed during ICU hospitalization in 57.5% of patients. Patients who remained adaptive had overall better prognosis, while those who remained inflammopathic or coagulopathic had worse overall outcomes. For severity metrics, patients admitted with sepsis and a high predicted likelihood of mortality showed an inflammopathic (49.6%) endotype and had higher rates of cumulative adverse outcomes (67.4%). Patients at low mortality risk, whether septic or non-septic, almost uniformly presented with an adaptive endotype (100% and 93.4%, respectively). Conclusion : Critically ill surgical patients express different and evolving immunological endotypes depending upon both their sepsis status and severity of their clinical course. Future studies will elucidate whether endotyping critically ill, septic patients can identify individuals for targeted therapeutic interventions to improve patient management and outcomes.

Project description:BACKGROUND:The post intensive care syndrome (PICS) gathers various disabilities, associated with a substantial healthcare use. However, patients' comorbidities and active medical conditions prior to intensive care unit (ICU) admission may partly drive healthcare use after ICU discharge. To better understand retative contribution of critical illness and PICS-compared to pre-existing comorbidities-as potential determinant of post-critical illness healthcare use, we conducted a population-based evaluation of patients' healthcare use trajectories. RESULTS:Using discharge databases in a 2.5-million-people region in France, we retrieved, over 3 years, all adult patients admitted in ICU for septic shock or acute respiratory distress syndrome (ARDS), intubated at least 5 days and discharged alive from hospital: 882 patients were included. Median duration of mechanical ventilation was 11 days (interquartile ranges [IQR] 8;20), mean SAPS2 was 49, and median hospital length of stay was 42 days (IQR 29;64). Healthcare use (days spent in healthcare facilities) was analyzed 2 years before and 2 years after ICU admission. Prior to ICU admission, we observed, at the scale of the whole study population, a progressive increase in healthcare use. Healthcare trajectories were then explored at individual level, and patients were assembled according to their individual pre-ICU healthcare use trajectory by clusterization with the K-Means method. Interestingly, this revealed diverse trajectories, identifying patients with elevated and increasing healthcare use (n?=?126), and two main groups with low (n?=?476) or no (n?=?251) pre-ICU healthcare use. In ICU, however, SAPS2, duration of mechanical ventilation and length of stay were not different across the groups. Analysis of post-ICU healthcare trajectories for each group revealed that patients with low or no pre-ICU healthcare (which represented 83% of the population) switched to a persistent and elevated healthcare use during the 2 years post-ICU. CONCLUSION:For 83% of ARDS/septic shock survivors, critical illness appears to have a pivotal role in healthcare trajectories, with a switch from a low and stable healthcare use prior to ICU to a sustained higher healthcare recourse 2 years after ICU discharge. This underpins the hypothesis of long-term critical illness and PICS-related quantifiable consequences in healthcare use, measurable at a population level.

Project description:BackgroundPatients who survive critical illness suffer from a significant physical disability. The impact of rehabilitation strategies on health-related quality of life is inconsistent, with population heterogeneity cited as one potential confounder. This secondary analysis aimed to (1) examine trajectories of functional recovery in critically ill patients to delineate sub-phenotypes and (2) to assess differences between these cohorts in both clinical characteristics and clinimetric properties of physical function assessment tools.MethodsTwo hundred ninety-one adult sepsis survivors were followed-up for 24 months by telephone interviews. Physical function was assessed using the Physical Component Score (PCS) of the Short Form-36 Questionnaire (SF-36) and Activities of Daily Living and the Extra Short Musculoskeletal Function Assessment (XSFMA-F/B). Longitudinal trajectories were clustered by factor analysis. Logistical regression analyses were applied to patient characteristics potentially determining cluster allocation. Responsiveness, floor and ceiling effects and concurrent validity were assessed within clusters.ResultsOne hundred fifty-nine patients completed 24 months of follow-up, presenting overall low PCS scores. Two distinct sub-cohorts were identified, exhibiting complete recovery or persistent impairment. A third sub-cohort could not be classified into either trajectory. Age, education level and number of co-morbidities were independent determinants of poor recovery (AUROC 0.743 ((95%CI 0.659-0.826), p < 0.001). Those with complete recovery trajectories demonstrated high levels of ceiling effects in physical function (PF) (15%), role physical (RP) (45%) and body pain (BP) (57%) domains of the SF-36. Those with persistent impairment demonstrated high levels of floor effects in the same domains: PF (21%), RP (71%) and BP (12%). The PF domain demonstrated high responsiveness between ICU discharge and at 6 months and was predictive of a persistent impairment trajectory (AUROC 0.859 (95%CI 0.804-0.914), p < 0.001).ConclusionsWithin sepsis survivors, two distinct recovery trajectories of physical recovery were demonstrated. Older patients with more co-morbidities and lower educational achievements were more likely to have a persistent physical impairment trajectory. In regard to trajectory prediction, the PF score of the SF-36 was more responsive than the PCS and could be considered for primary outcomes. Future trials should consider adaptive trial designs that can deal with non-responders or sub-cohort specific outcome measures more effectively.

Project description:Importance:Survivorship involves a multidisciplinary approach to surveillance and management of comorbidities and secondary cancers, overseen by oncologists, surgeons, and primary care physicians. Optimal timing and coordination of care, however, is unclear and often based on arbitrary 5-year cutoffs. Objective:To determine high- and low-risk periods for all tumor types that could define when survivorship care might best be overseen by oncologists and when to transition to primary care physicians. Design, Setting, and Participants:In this pan-cancer, longitudinal, observational study, excess mortality hazard, calculated as an annualized mortality risk above a baseline population, was plotted over time. The time this hazard took to stabilize defined a high-risk period. The percent morality elevation above age- and sex-matched controls in the latter low-risk period was reported as a mortality gap. The US population-based Surveillance, Epidemiology, and End Results database defined the cancer population, and the US Census life tables defined controls. Incident cases of patients with cancer were separated into tumor types based on International Classification of Diseases for Oncology definitions. Exposures:Population-level data on incident cancer cases was compared with the general US population. Main Outcomes and Measures:Overall mortality and cause of death were reported on observed cancer cases. Results:A total of 2 317 185 patients (median age, 63 years; 49.8% female) with 66 primary tumor types were evaluated. High-risk surveillance period durations ranged from less than 1 year (breast, prostate, lip, ocular, and parathyroid cancers) up to 19 years (unspecified gastrointestinal cancers). The annualized mortality gap, representing the excess mortality in the stable period, ranged from a median 0.26% to 9.33% excess annual mortality (thyroid and hypopharyngeal cancer populations, respectively). Cluster analysis produced 6 risk cluster groups: group 1, with median survival of 16.2 (5th to 95th percentile range [PR], 10.7-40.2) years and median high-risk period of 2.5 (PR, 0-5.0) years; group 2, 8.3 (PR, 5.1-23.3) and 2.5 (PR, 4.0-8.0) years; group 3, 2.8 (PR, 1.4-3.7) and 7.0 (PR, 6.0-11.1) years; group 4, 1.6 (PR, 1.5-1.8) and 6.0 (PR, 5.1-11.4) years; group 5, 0.8 (PR, 0.5-1.2) and 0.8 (PR, 0.5-1.2) years; and group 6, 0.5 (PR, 0.4-0.8) and 12.0 (PR, 9.3-12.9) years, respectively. Subanalyses of selected tumor types in these groups revealed that stratifying on stage and histologic type can change the risk cluster and guidance for care. Conclusions and Relevance:These findings indicate that a standardized 5-year surveillance period is inadequate for some cancers while excessive for others. High-risk cancers require the most resources with the longest high-risk period, highest persistent baseline mortality risk, and longest period of primary cancer mortality, all arguing for longer follow-up with an oncologist in these cancers.

Project description:ObjectiveTo analyze serial biomarkers of the persistent inflammation, immunosuppression, and catabolism syndrome (PICS) to gain insight into the pathobiology of chronic critical illness (CCI) after surgical sepsis.BackgroundAlthough early deaths after surgical intensive care unit sepsis have decreased and most survivors rapidly recover (RAP), one third develop the adverse clinical trajectory of CCI. However, the underlying pathobiology of its dismal long-term outcomes remains unclear.MethodsPICS biomarkers over 14 days from 124 CCI and 225 RAP sepsis survivors were analyzed to determine associations and prediction models for (1) CCI (≥14 intensive care unit days with organ dysfunction) and (2) dismal 1-year outcomes (Zubrod 4/5 performance scores). Clinical prediction models were created using PIRO variables (predisposition, insult, response, and organ dysfunction). Biomarkers were then added to determine if they strengthened predictions.ResultsCCI (vs RAP) and Zubrod 4/5 (vs Zubrod 0-3) cohorts had greater elevations in biomarkers of inflammation (interleukin [IL]-6, IL-8, interferon gamma-induced protein [IP-10], monocyte chemoattractant protein 1), immunosuppression (IL-10, soluble programmed death ligand-1), stress metabolism (C-reactive protein, glucagon-like peptide 1), and angiogenesis (angiopoietin-2, vascular endothelial growth factor, vascular endothelial growth factor receptor-1, stromal cell-derived factor) at most time-points. Clinical models predicted CCI on day 4 (area under the receiver operating characteristics curve [AUC] = 0.89) and 1 year Zubrod 4/5 on day 7 (AUC = 0.80). IL-10 and IP-10 on day 4 minimally improved prediction of CCI (AUC = 0.90). However, IL-10, IL-6, IL-8, monocyte chemoattractant protein 1, IP-10, angiopoietin-2, glucagon-like peptide 1, soluble programmed death ligand-1, and stromal cell-derived factor on day 7 considerably improved the prediction of Zubrod 4/5 status (AUC = 0.88).ConclusionsPersistent elevations of PICS biomarkers in the CCI and Zubrod 4/5 cohorts and their improved prediction of Zubrod 4/5 validate that PICS plays a role in CCI pathobiology.

Project description:PurposePatients with persistent critical illness may account for up to half of all intensive care unit (ICU) bed-days. It is unknown if there is hospital variation in the development of persistent critical illness and if hospital performance affects the incidence of persistent critical illness.MethodsThis is a retrospective analysis of Veterans admitted to the Veterans Administration (VA) ICUs from 2015 to 2017. Hospital performance was defined by the risk- and reliability-adjusted 30-day mortality. Persistent critical illness was defined as an ICU length of stay of at least 11 days. We used 2-level multilevel logistic regression models to assess variation in risk- and reliability-adjusted probabilities in the development of persistent critical illness.ResultsIn the analysis of 100 hospitals which encompassed 153,512 hospitalizations, 4.9% (N = 7640/153,512) developed persistent critical illness. There was variation in the development of persistent critical illness despite controlling for patient characteristics (intraclass correlation: 0.067, 95% CI 0.049-0.091). Hospitals with higher risk- and reliability-adjusted 30-day mortality had higher probabilities of developing persistent critical illness (predicted probability: 0.057, 95% CI 0.051-0.063, p < 0.01) compared to those with lower risk- and reliability-adjusted 30-day mortality (predicted probability: 0.046, 95% CI 0.041-0.051, p < 0.01). The median odds ratio was 1.4 (95% CI 1.33-1.49) implying that, for two patients with the same physiology on admission at two different VA hospitals, the patient admitted to the hospital with higher adjusted mortality would have 40% greater odds of developing persistent critical illness.ConclusionHospitals with higher risk- and reliability-adjusted 30-day mortality have a higher probability of developing persistent critical illness. Understanding the drivers of this variation may identify modifiable factors contributing to the development of persistent critical illness.

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Defining persistent critical illness based on growth trajectories in patients with sepsis.

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Defining persistent critical illness based on growth trajectories in patients with sepsis.

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