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Nationwide retrospective study of critically ill adults with sickle cell disease in France.

ABSTRACT: Little is known about patients with sickle cell disease (SCD) who require intensive care unit (ICU) admission. The goals of this study were to assess outcomes in patients admitted to the ICU for acute complications of SCD and to identify factors associated with adverse outcomes. This multicenter retrospective study included consecutive adults with SCD admitted to one of 17 participating ICUs. An adverse outcome was defined as death or a need for life-sustaining therapies (non-invasive or invasive ventilation, vasoactive drugs, renal replacement therapy, and/or extracorporeal membrane oxygenation). Factors associated with adverse outcomes were identified by mixed multivariable logistic regression. We included 488 patients admitted in 2015-2017. The main reasons for ICU admission were acute chest syndrome (47.5%) and severely painful vaso-occlusive event (21.3%). Sixteen (3.3%) patients died in the ICU, mainly of multi-organ failure following a painful vaso-occlusive event or sepsis. An adverse outcome occurred in 81 (16.6%; 95% confidence interval [95% CI], 13.3%-19.9%) patients. Independent factors associated with adverse outcomes were low mean arterial blood pressure (adjusted odds ratio [aOR], 0.98; 95% CI 0.95-0.99; p = 0.027), faster respiratory rate (aOR, 1.09; 95% CI 1.05-1.14; p < 0.0001), higher haemoglobin level (aOR, 1.22; 95% CI 1.01-1.48; p = 0.038), impaired creatinine clearance at ICU admission (aOR, 0.98; 95% CI 0.97-0.98; p < 0.0001), and red blood cell exchange before ICU admission (aOR, 5.16; 95% CI 1.16-22.94; p = 0.031). Patients with SCD have a substantial risk of adverse outcomes if they require ICU admission. Early ICU admission should be encouraged in patients who develop abnormal physiological parameters.

SUBMITTER: Agbakou M

PROVIDER: S-EPMC8632921 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Systemic inflammation is a whole body reaction that can have an infection-positive (i.e. sepsis) or infection-negative origin. It is important to distinguish between septic and non-septic presentations early and reliably, because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on a small number of RNAs expressed in peripheral blood could be discovered that would: 1) determine which patients with systemic inflammation had sepsis; 2) be robust across independent patient cohorts; 3) be insensitive to disease severity; and 4) provide diagnostic utility. The overall goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICU). Biomarker discovery was conducted with an Australian cohort (n = 105) consisting of sepsis patients and post -surgical patients with infection-negative systemic inflammation. Using this cohort, a four-gene classifier consisting of a combination of CEACAM4, LAMP1, PLA2G7 and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyteÂ® Lab, was externally validated using RT-qPCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Cohort 1 (n=59) consisted of unambiguous septic cases and infection-negative systemic inflammation controls; SeptiCyteÂ® Lab gave an area under curve (AUC) of 0.96 (95% CI: 0.91-1.00). ROC analysis of a more heterogeneous group of patients (Cohorts 2-5; 249 patients after excluding 37 patients with infection likelihood possible) gave an AUC of 0.89 (95% CI: 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or the Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility o f SeptiCyteÂ® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyteÂ® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyteÂ® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyteÂ® Lab is a rapid molecular assay that may be clinically useful in the management of ICU patients with systemic inflammation. SIRS and Sepsis ICU patients, admission samples Retrospective, mutli-site sutdy using retrospective physician adjudication as a comparator

Project description:BACKGROUND:Sickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications, including pain episodes, stroke, and kidney disease. Patients with SCD develop chronic organ dysfunction, which may progress to organ failure during disease exacerbations. Early detection of acute physiological deterioration leading to organ failure is not always attainable. Machine learning techniques that allow for prediction of organ failure may enable early identification and treatment and potentially reduce mortality. OBJECTIVE:The aim of this study was to test the hypothesis that machine learning physiomarkers can predict the development of organ dysfunction in a sample of adult patients with SCD admitted to intensive care units (ICUs). METHODS:We applied diverse machine learning methods, statistical methods, and data visualization techniques to develop classification models to distinguish SCD from controls. RESULTS:We studied 63 sequential SCD patients admitted to ICUs with 163 patient encounters (mean age 30.7 years, SD 9.8 years). A subset of these patient encounters, 22.7% (37/163), met the sequential organ failure assessment criteria. The other 126 SCD patient encounters served as controls. A set of signal processing features (such as fast Fourier transform, energy, and continuous wavelet transform) derived from heart rate, blood pressure, and respiratory rate was identified to distinguish patients with SCD who developed acute physiological deterioration leading to organ failure from patients with SCD who did not meet the criteria. A multilayer perceptron model accurately predicted organ failure up to 6 hours before onset, with an average sensitivity and specificity of 96% and 98%, respectively. CONCLUSIONS:This retrospective study demonstrated the viability of using machine learning to predict acute organ failure among hospitalized adults with SCD. The discovery of salient physiomarkers through machine learning techniques has the potential to further accelerate the development and implementation of innovative care delivery protocols and strategies for medically vulnerable patients.

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Project description:ContextSickle cell disease (SCD) is a severe hematological disorder. The most common acute complication of SCD is vaso-occlusive crisis (VOC), but SCD is a systemic disease potentially involving all organs. SCD prevalence estimates rely mostly on extrapolations from incidence-based newborn screening programs, although recent improvements in survival may have led to an increase in prevalence, and immigration could account for a substantial number of prevalent patients in Europe. The primary objective of this study was to estimate SCD prevalence in France.MethodsA cross-sectional observational study was conducted using a representative sample of national health insurance data. SCD patients followed up in France between 2006 and 2011 were captured through hydroxyurea reimbursement and with the International Classification of Diseases (ICD-10) SCD specific code D570.1.2, excluding code D573 (which corresponds to sickle cell trait (SCT)). Nevertheless, we assumed that ICD-10 diagnosis coding for inpatient stays could be imperfect, with the possibility of SCT being miscoded as SCD. Therefore, prevalence was analyzed in two groups of patients [with at least one (G1) or two (G2) inpatient stay] based on the number of SCD-related inpatient stays in the six-year study period, assuming that SCT patients are rarely rehospitalized compared to SCD. The prevalence of SCD in the sample, which was considered to be representative of the French population, was then extrapolated to the general population. The rate of vaso-occlusive crisis (VOC) events was estimated based on hospitalizations, emergencies, opioid reimbursements, transfusions, and sick leave.ResultsBased on the number of patients identified for G1 and G2, the 2016 French prevalence was estimated to be between 48.6 per 100,000 (G1) or 32,400 patients and 29.7 per 100,000 (G2) or 19,800 patients. An average of 1.51 VOC events per year were identified, with an increase frequency of 15 to 24 years of age. The average annual number of hospitalizations was between 0.70 (G1) and 1.11 (G2) per patient. Intensive care was observed in 7.6% of VOC-related hospitalizations. Fewer than 34% of SCD patients in our sample received hydroxyurea at any point in their follow-up. The annual average cost of SCD care is €5,528.70 (G1) to €6,643.80 (G2), with most costs arising from hospitalization and lab testing.ConclusionOur study estimates SCD prevalence in France at between 19,800 and 32,400 patients in 2016, higher than previously published. This study highlights the significant disease burden associated with vaso-occlusive events.

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Nationwide retrospective study of critically ill adults with sickle cell disease in France.

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