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Prediction of critical illness during out-of-hospital emergency care.

ABSTRACT: Early identification of nontrauma patients in need of critical care services in the emergency setting may improve triage decisions and facilitate regionalization of critical care.To determine the out-of-hospital clinical predictors of critical illness and to characterize the performance of a simple score for out-of-hospital prediction of development of critical illness during hospitalization.Population-based cohort study of an emergency medical services (EMS) system in greater King County, Washington (excluding metropolitan Seattle), that transports to 16 receiving facilities.Nontrauma, non-cardiac arrest adult patients transported to a hospital by King County EMS from 2002 through 2006. Eligible records with complete data (N = 144,913) were linked to hospital discharge data and randomly split into development (n = 87,266 [60%]) and validation (n = 57,647 [40%]) cohorts.Development of critical illness, defined as severe sepsis, delivery of mechanical ventilation, or death during hospitalization.Critical illness occurred during hospitalization in 5% of the development (n = 4835) and validation (n = 3121) cohorts. Multivariable predictors of critical illness included older age, lower systolic blood pressure, abnormal respiratory rate, lower Glasgow Coma Scale score, lower pulse oximetry, and nursing home residence during out-of-hospital care (P < .01 for all). When applying a summary critical illness prediction score to the validation cohort (range, 0-8), the area under the receiver operating characteristic curve was 0.77 (95% confidence interval [CI], 0.76-0.78), with satisfactory calibration slope (1.0). Using a score threshold of 4 or higher, sensitivity was 0.22 (95% CI, 0.20-0.23), specificity was 0.98 (95% CI, 0.98-0.98), positive likelihood ratio was 9.8 (95% CI, 8.9-10.6), and negative likelihood ratio was 0.80 (95% CI, 0.79- 0.82). A threshold of 1 or greater for critical illness improved sensitivity (0.98; 95% CI, 0.97-0.98) but reduced specificity (0.17; 95% CI, 0.17-0.17).In a population-based cohort, the score on a prediction rule using out-of-hospital factors was significantly associated with the development of critical illness during hospitalization. This score requires external validation in an independent population.

SUBMITTER: Seymour CW

PROVIDER: S-EPMC3949007 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Prediction of critical illness during out-of-hospital emergency care.

Seymour Christopher W CW Kahn Jeremy M JM Cooke Colin R CR Watkins Timothy R TR Heckbert Susan R SR Rea Thomas D TD

JAMA 20100801 7

<h4>Context</h4>Early identification of nontrauma patients in need of critical care services in the emergency setting may improve triage decisions and facilitate regionalization of critical care.<h4>Objectives</h4>To determine the out-of-hospital clinical predictors of critical illness and to characterize the performance of a simple score for out-of-hospital prediction of development of critical illness during hospitalization.<h4>Design and setting</h4>Population-based cohort study of an emergen ...[more]

PMID: 20716737

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Project description:Rationale: A small but growing number of hospitals are experimenting with emergency department-embedded critical care units (CCUs) in an effort to improve the quality of care for critically ill patients with sepsis and acute respiratory failure (ARF).Objectives: To evaluate the potential impact of an emergency department-embedded CCU at the Hospital of the University of Pennsylvania among patients with sepsis and ARF admitted from the emergency department to a medical ward or intensive care unit (ICU) from January 2016 to December 2017.Methods: The exposure was eligibility for admission to the emergency department-embedded CCU, which was defined as meeting a clinical definition for sepsis or ARF and admission to the emergency department during the intervention period on a weekday. The primary outcome was hospital length of stay (LOS); secondary outcomes included total emergency department plus ICU LOS, hospital survival, direct admission to the ICU, and unplanned ICU admission. Primary interrupted time series analyses were performed using ordinary least squares regression comparing monthly means. Secondary retrospective cohort and before-after analyses used multivariable Cox proportional hazard and logistic regression.Results: In the baseline and intervention periods, 3,897 patients met the inclusion criteria for sepsis and 1,865 patients met the criteria for ARF. Among patients admitted with sepsis, opening of the emergency department-embedded CCU was not associated with hospital LOS (β = -1.82 d; 95% confidence interval [CI], -4.50 to 0.87; P = 0.17 for the first month after emergency department-embedded CCU opening compared with baseline; β = -0.26 d; 95% CI, -0.58 to 0.06; P = 0.10 for subsequent months). Among patients admitted with ARF, the emergency department-embedded CCU was not associated with a significant change in hospital LOS for the first month after emergency department-embedded CCU opening (β = -3.25 d; 95% CI, -7.86 to 1.36; P = 0.15) but was associated with a 0.64 d/mo shorter hospital LOS for subsequent months (β = -0.64 d; 95% CI, -1.12 to -0.17; P = 0.01). This result persisted among higher acuity patients requiring ventilatory support but was not supported by alternative analytic approaches. Among patients admitted with sepsis who did not require mechanical ventilation or vasopressors in the emergency department, the emergency department-embedded CCU was associated with an initial 9.9% reduction in direct ICU admissions in the first month (β = -0.099; 95% CI, -0.153 to -0.044; P = 0.002), followed by a 1.1% per month increase back toward baseline in subsequent months (β = 0.011; 95% CI, 0.003-0.019; P = 0.009). This relationship was supported by alternative analytic approaches and was not seen in ARF. No associations with emergency department plus ICU LOS, hospital survival, or unplanned ICU admission were observed among patients with sepsis or ARF.Conclusions: The emergency department-embedded CCU was not associated with clinical outcomes among patients admitted with sepsis or ARF. Among less sick patients with sepsis, the emergency department-embedded CCU was initially associated with reduced rates of direct ICU admission from the emergency department. Additional research is necessary to further evaluate the impact and utility of the emergency department-embedded CCU model.

Project description:Accurately measuring the risk of pressure injury remains the most important step for effective prevention and intervention. Time-dependent risk factors for pressure injury development in the adult intensive care unit setting are not well understood.ObjectivesTo develop and validate a dynamic risk prediction model to estimate the risk of developing a hospital-acquired pressure injury among adult ICU patients.DesignICU admission data were split into training and validation sets. With death as a competing event, both static and dynamic Fine-Gray models were developed to predict hospital-acquired pressure injury development less than 24, 72, and 168 hours postadmission. Model performance was evaluated using Wolbers' concordance index, Brier score, net reclassification improvement, and integrated discrimination improvement.Setting and participantsWe performed a retrospective cohort study of ICU patients in a tertiary care hospital located in San Francisco, CA, from November 2013 to August 2017.Main outcomes and measuresData were extracted from electronic medical records of 18,019 ICU patients (age ≥ 18 yr; 21,220 encounters). Record of hospital-acquired pressure injury data was captured in our institution's incident reporting system. The information is periodically reviewed by our wound care team. Presence of hospital-acquired pressure injury during an encounter and hospital-acquired pressure injury diagnosis date were provided.ResultsThe dynamic model predicting hospital-acquired pressure injury more than 24 hours postadmission, including predictors age, body mass index, lactate serum, Braden scale score, and use of vasopressor and antifungal medications, had adequate discrimination ability within 6 days from time of prediction (c = 0.73). All dynamic models produced more accurate risk estimates than static models within 26 days postadmission. There were no significant differences in Brier scores between dynamic and static models.Conclusions and relevanceA dynamic risk prediction model predicting hospital-acquired pressure injury development less than 24 hours postadmission in ICU patients for up to 7 days postadmission was developed and validated using a large dataset of clinical variables readily available in the electronic medical record.

Project description:BackgroundCritical illness is often accompanied by hypercortisolemia, which has been attributed to stress-induced activation of the hypothalamic-pituitary-adrenal axis. However, low corticotropin levels have also been reported in critically ill patients, which may be due to reduced cortisol metabolism.MethodsIn a total of 158 patients in the intensive care unit and 64 matched controls, we tested five aspects of cortisol metabolism: daily levels of corticotropin and cortisol; plasma cortisol clearance, metabolism, and production during infusion of deuterium-labeled steroid hormones as tracers; plasma clearance of 100 mg of hydrocortisone; levels of urinary cortisol metabolites; and levels of messenger RNA and protein in liver and adipose tissue, to assess major cortisol-metabolizing enzymes.ResultsTotal and free circulating cortisol levels were consistently higher in the patients than in controls, whereas corticotropin levels were lower (P<0.001 for both comparisons). Cortisol production was 83% higher in the patients (P=0.02). There was a reduction of more than 50% in cortisol clearance during tracer infusion and after the administration of 100 mg of hydrocortisone in the patients (P≤0.03 for both comparisons). All these factors accounted for an increase by a factor of 3.5 in plasma cortisol levels in the patients, as compared with controls (P<0.001). Impaired cortisol clearance also correlated with a lower cortisol response to corticotropin stimulation. Reduced cortisol metabolism was associated with reduced inactivation of cortisol in the liver and kidney, as suggested by urinary steroid ratios, tracer kinetics, and assessment of liver-biopsy samples (P≤0.004 for all comparisons).ConclusionsDuring critical illness, reduced cortisol breakdown, related to suppressed expression and activity of cortisol-metabolizing enzymes, contributed to hypercortisolemia and hence corticotropin suppression. The diagnostic and therapeutic implications for critically ill patients are unknown. (Funded by the Belgian Fund for Scientific Research and others; ClinicalTrials.gov numbers, NCT00512122 and NCT00115479; and Current Controlled Trials numbers, ISRCTN49433936, ISRCTN49306926, and ISRCTN08083905.).

Project description:BACKGROUND:Out-of-hours (OOH) healthcare services in Western countries are often differentiated into out-of-hours primary healthcare services (OOH-PC) and emergency medical services (EMS). Call waiting time, triage model and intended aims differ between these services. Consequently, the care pathway and outcome could vary based on the choice of entrance to the healthcare system. We aimed to investigate patient pathways and 1- and 1-30-day mortality, intensive care unit (ICU) stay and length of hospital stay for patients with acute myocardial infarction (AMI), stroke and sepsis in relation to the OOH service that was contacted prior to the hospital contact. METHODS:Population-based observational cohort study during 2016 including adult patients from two Danish regions with an OOH service contact on the date of hospital contact. Patients <18?years were excluded. Data was retrieved from OOH service databases and national registries, linked by a unique personal identification number. Crude and adjusted logistic regression analyses were performed to assess mortality in relation to contacted OOH service with OOH-PC as the reference and cox regression analysis to assess risk of ICU stay. RESULTS:We included 6826 patients. AMI and stroke patients more often contacted EMS (52.1 and 54.1%), whereas sepsis patients predominately called OOH-PC (66.9%). Less than 10% (all diagnoses) of patients contacted both OOH-PC & EMS. Stroke patients with EMS or OOH-PC & EMS contacts had higher likelihood of 1- and 1-30-day mortality, in particular 1-day (EMS: OR?=?5.33, 95% CI: 2.82-10.08; OOH-PC & EMS: OR?=?3.09, 95% CI: 1.06-9.01). Sepsis patients with EMS or OOH-PC & EMS contacts also had higher likelihood of 1-day mortality (EMS: OR?=?2.22, 95% CI: 1.40-3.51; OOH-PC & EMS: OR?=?2.86, 95% CI: 1.56-5.23) and 1-30-day mortality. Risk of ICU stay was only significantly higher for stroke patients contacting EMS (EMS: HR?=?2.38, 95% CI: 1.51-3.75). Stroke and sepsis patients with EMS contact had longer hospital stays. CONCLUSIONS:More patients contacted OOH-PC than EMS. Sepsis and stroke patients contacting EMS solely or OOH-PC & EMS had higher likelihood of 1- and 1-30-day mortality during the subsequent hospital contact. Our results suggest that patients contacting EMS are more severely ill, however OOH-PC is still often used for time-critical conditions.

Project description:ObjectiveAs 'critical illness' and 'critical care' lack consensus definitions, this study aimed to explore how the concepts' are used, describe their defining attributes, and propose potential definitions.Design and methodsWe used the Walker and Avant approach to concept analysis. The uses and definitions of the concepts were identified through a scoping review of the literature and an online survey of 114 global clinical experts. We used the Arksey and O'Malley framework for scoping reviews and searched in PubMed and Web of Science with a strategy including terms around critical illness/care and definitions/etymologies limited to publications in English between 1 January 2008 and 1 January 2020. The experts were selected through purposive sampling and snowballing, with 36.8% in Africa, 25.4% in Europe, 22.8% in North America, 10.5% in Asia, 2.6% in South America and 1.8% in Australia. They worked with anaesthesia or intensive care 59.1%, emergency care 15.8%, medicine 9.5%, paediatrics 5.5%, surgery 4.7%, obstetrics and gynaecology 1.6% and other specialties 3.9%. Through content analysis of the data, we extracted codes, categories and themes to determine the concepts' defining attributes and we proposed potential definitions. To assist understanding, we developed model, related and contrary cases concerning the concepts, we identified antecedents and consequences to the concepts, and defined empirical referents.ResultsNine and 13 articles were included in the scoping reviews of critical illness and critical care, respectively. A total of 48 codes, 14 categories and 4 themes were identified in the uses and definitions of critical illness and 60 codes, 13 categories and 5 themes for critical care. The defining attributes of critical illness were a high risk of imminent death; vital organ dysfunction; requirement for care to avoid death; and potential reversibility. The defining attributes of critical care were the identification, monitoring and treatment of critical illness; vital organ support; initial and sustained care; any care of critical illness; and specialised human and physical resources. The defining attributes led to our proposed definitions of critical illness as, 'a state of ill health with vital organ dysfunction, a high risk of imminent death if care is not provided and the potential for reversibility', and of critical care as, 'the identification, monitoring and treatment of patients with critical illness through the initial and sustained support of vital organ functions.'ConclusionThe concepts critical illness and critical care lack consensus definitions and have varied uses. Through concept analysis of uses and definitions in the literature and among experts, we have identified the defining attributes of the concepts and proposed definitions that could aid clinical practice, research and policy-making.

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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Prediction of critical illness during out-of-hospital emergency care.

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Prediction of critical illness during out-of-hospital emergency care.

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