• ECG
• capnography
• cardiopulmonary arrest
• electrolytes
• pulse oximetry

• Animals
• Dogs
• Cats
• Cardiopulmonary Resuscitation/veterinary
• Cardiopulmonary Resuscitation/standards
• Cardiopulmonary Resuscitation/methods
• Cat Diseases/therapy
• Heart Arrest/veterinary
• Heart Arrest/therapy
• Veterinary Medicine/standards
• Dog Diseases/therapy
• Monitoring, Physiologic/veterinary
• Monitoring, Physiologic/standards

• Boehringer Ingelheim Animal Health
• Zoetis Animal Health

• cardiac arrest
• early warning score
• hospital rapid response team
• intensive care units
• pediatrics

• EEG monitoring
• cardiac arrest
• death
• hypoxic-ischemic encephalopathy
• seizure

• MEWS
• clinical deterioration
• internal medicine ward
• nursing role
• outcomes
• patient safety

• acute functional decline
• clinical decision-making
• clinical judgement
• modified early warning scores
• observational study
• vital signs

• Post-concussion
• concussion elderly
• labyrinthine concussion
• multiple canal BPPV

• Emergency
• Lactate
• MEWS
• NEWS
• Older people
• Point-of-care
• Poor outcome
• qSOFA

• Aged
• Emergency Service, Hospital
• Hospital Mortality
• Humans
• Lactic Acid
• Point-of-Care Systems
• Prevalence
• Prospective Studies
• ROC Curve
• Retrospective Studies
• Sepsis

• University of Lausanne

• Architecture
• COVID-19
• Consent
• IoT
• NEWS-2
• Remote monitoring

Recent years have witnessed increasing numbers of artificial intelligence (AI) based applications and devices being tested and approved for medical care. Diabetes is arguably the most common chronic disorder worldwide and AI is now being used for making an early diagnosis, to predict and diagnose early complications, increase adherence to therapy, and even motivate patients to manage diabetes and maintain glycemic control. However, these AI applications have largely been tested in non-critically ill patients and aid in managing chronic problems. Intensive care units (ICUs) have a dynamic environment generating huge data, which AI can extract and organize simultaneously, thus analysing many variables for diagnostic and/or therapeutic purposes in order to predict outcomes of interest. Even non-diabetic ICU patients are at risk of developing hypo or hyperglycemia, complicating their ICU course and affecting outcomes. In addition, to maintain glycemic control frequent blood sampling and insulin dose adjustments are required, increasing nursing workload and chances of error. AI has the potential to improve glycemic control while reducing the nursing workload and errors. Continuous glucose monitoring (CGM) devices, which are Food and Drug Administration (FDA) approved for use in non-critically ill patients, are now being recommended for use in specific ICU populations with increased accuracy. AI based devices including artificial pancreas and CGM regulated insulin infusion system have shown promise as comprehensive glycemic control solutions in critically ill patients. Even though many of these AI applications have shown potential, these devices need to be tested in larger number of ICU patients, have wider availability, show favorable cost-benefit ratio and be amenable for easy integration into the existing healthcare systems, before they become acceptable to ICU physicians for routine use.

• Artificial intelligence
• Blood glucose
• Critical care
• Diabetes mellitus
• Intensive care unit
• Machine learning

• Sepsis
• diagnostic accuracy
• early warning score
• inpatients
• older adults
• qSOFA

Knowledge about psychological distress in older cardiac arrest (CA) survivors is sparse, and the lack of comparisons with general populations make it difficult to draw any strong conclusions about prevalence and potential changes caused by CA. Our aim was to compare psychological distress between older CA survivors and a general population. This study included survivors 65–80 years old and an age- and sex-matched general population. Data on survivors was collected from the Swedish Register of Cardiopulmonary Resuscitation. The Hospital Anxiety and Depression Scale was used to measure psychological distress. Data were analyzed with non-parametric statistics. The final sample included 1027 CA survivors and 1018 persons from the general population. In both groups, the mean age was 72 years (SD = 4) and 28% were women. The prevalence of anxiety was 9.9% for survivors and 9.5% for the general population, while the corresponding prevalence for depression was 11.3% and 11.5% respectively. Using the cut-off scores, no significant differences between the groups were detected. However, CA survivors reported significantly lower symptom levels using the subscale scores (ΔMdn = 1, p < 0.001). In conclusion, the CA survivors did not report higher symptom levels of anxiety and depression than the general population. However, since psychological distress is related to poor quality-of-life and recovery, screening for psychological distress remains important.

• aged
• anxiety
• depression
• heart arrest
• psychological distress

• Early warning score
• General wards
• In-hospital cardiac arrest
• Incidence

• Aged
• Aged, 80 and over
• Cardiopulmonary Resuscitation
• Denmark/epidemiology
• Early Warning Score
• Female
• Heart Arrest/epidemiology
• Hospitals
• Humans
• Incidence
• Male
• Middle Aged
• Patients' Rooms

• Acute Kidney Injury/diagnosis
• Acute Kidney Injury/epidemiology
• Aged
• Aged, 80 and over
• Clinical Decision-Making/methods
• Critical Illness/mortality
• Critical Illness/therapy
• Early Warning Score
• Emergency Service, Hospital/statistics & numerical data
• Female
• Frailty/diagnosis
• Frailty/epidemiology
• Hospital Mortality
• Humans
• Male
• Patient Admission/statistics & numerical data
• Prospective Studies
• Risk Assessment/methods
• Risk Assessment/statistics & numerical data
• United Kingdom

• breathing control
• measurement scenario
• patient monitoring
• respiratory frequency
• respiratory monitoring
• sensors
• stress
• technologies
• vital signs
• wearables

• Delivery of Health Care
• Exercise
• Humans
• Monitoring, Physiologic
• Respiratory Rate
• Sports Medicine
• Vital Signs

• early warning score
• implementation science
• nursing homes
• qualitative research
• residential facilities

• clinical decision rules
• critical care
• deep learning
• machine learning
• operations
• triage

• R01 GM114355 NIGMS NIH HHS

There has been an increasing demand for emergency medical services (EMS), and a growing number of patients are not conveyed; i.e., they are referred to levels of care other than ambulance conveyance to the emergency department. Patient safety issues have been raised regarding the ability of EMS to decide not to convey patients. To improve non-conveyance guidelines, information is needed about patients who are not conveyed by EMS. Therefore, the purpose of this study was to describe and compare the proportion and characteristics of non-conveyed EMS patients, together with assignment data.

A descriptive and comparative consecutive cohort design was undertaken. The decision of whether to convey patients was made by EMS according to a region-specific non-conveyance guideline. Non-conveyed patients’ medical record data were prospectively gathered from February 2016 to January 2017. Analyses was conducted using the chi-squared test, two-sample t test, proportion test and Mann-Whitneys U-test.

Out of the 23,250 patients served during the study period, 2691 (12%) were not conveyed. For non-conveyed adults, the most commonly used Emergency Signs and Symptoms (ESS) codes were unspecific symptoms/malaise, abdomen/flank/groin pain, and breathing difficulties. For non-conveyed children, the most common ESS codes were breathing difficulties and fever of unclear origin. Most of the non-conveyed patients had normal vital signs. Half of all patients with a designated non-conveyance level of care were referred to self-care. There were statistically significant differences between men and women.

Fewer patients were non-conveyed in the studied region compared to national and international non-conveyance rates. The differences seen between men and women were not of clinical significance. Follow-up studies are needed to understand what effect patient outcome so that guidelines might improve.

• Ambulance
• Emergency medical services
• Non-conveyance
• Non-transport
• Triage

• Aged
• Female
• Heart Arrest/diagnosis
• Hospitalization
• Humans
• Machine Learning
• Male
• Middle Aged
• Prognosis
• Retrospective Studies
• Vital Signs

Elderly patients have a blunted host response, which may influence vital signs and clinical outcomes of sepsis. This study was aimed to investigate whether the associations between the vital signs and mortality are different in elderly and non-elderly patients with sepsis.

This was a retrospective observational study. A Japanese multicenter sepsis cohort (FORECAST, n = 1148) was used for the discovery analyses. Significant discovery results were tested for replication using two validation cohorts of sepsis (JAAMSR, Japan, n = 624; SPH, Canada, n = 1004). Patients were categorized into elderly and non-elderly groups (age ≥ 75 or < 75 years). We tested for association between vital signs (body temperature [BT], heart rate, mean arterial pressure, systolic blood pressure, and respiratory rate) and 90-day in-hospital mortality (primary outcome).

In the discovery cohort, non-elderly patients with BT < 36.0 °C had significantly increased 90-day mortality (P = 0.025, adjusted hazard ratio 1.70, 95% CI 1.07–2.71). In the validation cohorts, non-elderly patients with BT < 36.0 °C had significantly increased mortality (JAAMSR, P = 0.0024, adjusted hazard ratio 2.05, 95% CI 1.29–3.26; SPH, P = 0.029, adjusted hazard ratio 1.36, 95% CI 1.03–1.80). These differences were not observed in elderly patients in the three cohorts. Associations between the other four vital signs and mortality were not different in elderly and non-elderly patients. The interaction of age and hypothermia/fever was significant (P < 0.05).

In septic patients, we found mortality in non-elderly sepsis patients was increased with hypothermia and decreased with fever. However, mortality in elderly patients was not associated with BT. These results illuminate the difference in the inflammatory response of the elderly compared to non-elderly sepsis patients.

• Body temperature
• Elderly
• Fever
• Hypothermia
• Septic shock

• Health services
• Hospital readmission
• Nursing
• Patient readmission
• Patient safety
• Rapid response team
• Rehabilitation

• ageing
• blood pressure
• hospitals
• physiology

• Adult
• Age Factors
• Aged
• Aged, 80 and over
• Blood Pressure/physiology
• Cross-Sectional Studies
• England
• Female
• Hospitalization
• Humans
• Male
• Middle Aged
• Retrospective Studies
• Sex Factors

• DRF-2016-09-073 Department of Health
• WT-103703/Z/14/Z Wellcome Trust
• HICF-R9-524 Department of Health [UK]
• National Institute for Health Research
• Rhodes Scholarships

The National Early Warning Score (NEWS), based on the patients’ vital signs, detects clinical deterioration in critically ill patients and is used to reduce the incidence of in-hospital cardiac arrest. However, although mortality prediction based on vital signs may be difficult in older patients, the effectiveness of the NEWS has not yet been evaluated in this population. This study aimed to test the hypothesis that an elevated NEWS at admission increases the mortality risk in older patients admitted to the emergency department (ED).

We conducted a single-center retrospective study, including patients admitted to the ED between November 2016 and February 2017. We included patients aged >65 years who were admitted to the ED for any medical problem. The NEWS was calculated at the time of ED admission. The primary outcome was in-hospital mortality.

In total, 3,169 patients were included in this study. Median age was 75 years (interquartile range [IQR], 70 to 80 years), and 1,557 (49.1%) patients were male. The in-hospital mortality rate was 5.1% (161 patients). Median NEWS was higher in non-survivors than in survivors (5 [IQR, 3–8] vs. 1 [IQR, 0–3], P<0.001). Multivariate logistic analysis showed that the NEWS was associated with in-hospital mortality, after adjusting for other confounders. The area under the curve of the NEWS for predicting in-hospital mortality was 0.820 (95% confidence interval, 0.806 to 0.833).

Our results show that the NEWS at admission is associated with in-hospital mortality among patients aged >65 years.

• Aged
• Mortality
• National Early Warning Score

Although scoring and machine learning methods have been developed to predict patient deterioration, bedside assessment by nurses should not be overlooked. This study aimed to evaluate the performance of subjective bedside assessment of the patient by the rapid response team (RRT) nurses in predicting short-term patient deterioration.

Patients noticed by RRT nurses based on the vital sign instability, abnormal laboratory results, and direct contact via phone between November 1, 2016, and December 12, 2017, were included. Five RRT nurses visited the patients according to their shifts and assessed the possibility of patient deterioration. Patient acuity rating (PAR), a scale of 1–7, was used as the tool of bedside assessment. Other scores, including the modified early warning score, VitalPAC early warning score, standardised early warning score, and cardiac arrest risk triage, were calculated afterwards. The performance of these scores in predicting mortality and/or intensive care unit admission within 1 day was compared by calculating the area under the receiver operating curve.

A total of 1,426 patients were included in the study, of which 258 (18.1%) died or were admitted to the intensive care unit within 1 day. The area under the receiver operating curve of PAR was 0.87 (95% confidence interval [CI] 0.84–0.89), which was higher than those of modified early warning score (0.66, 95% CI 0.62–0.70), VitalPAC early warning score (0.69, 95% CI 0.66–0.73), standardised early warning score (0.67, 95% CI 0.63–0.70) and cardiac arrest risk triage (0.63, 95% CI 0.59–0.66) (P<0.001).

PAR assessed by RRT nurses can be a useful tool for assessing short-term patient prognosis in the RRT setting.

• aging
• comorbidity
• octogenarians
• respiratory rate
• vital signs

• Inpatient mortality
• electronic health records (EHR)
• emergency department (ED)
• predictive model

• Adult
• Aged
• Aged, 80 and over
• Area Under Curve
• Electronic Health Records
• Emergency Service, Hospital
• Female
• Hospital Mortality
• Hospitalization
• Humans
• Inpatients
• Logistic Models
• Male
• Middle Aged
• Models, Statistical
• Patient Admission
• ROC Curve
• Retrospective Studies
• Risk Assessment
• Risk Factors
• Singapore
• Tertiary Care Centers
• Triage

• Duke-NUS Medical School and the Estate of Tan Sri Khoo Teck Puat

Medicine is still very inadequate in forecasting recovery of tipping points in health and disease, especially in older adults. However, increasingly, diseases and invasive treatments unexpectedly push older patients with low resilience over their tipping points (TPs). These TPs are the points in human physiology that separate more healthy conditions from disease conditions or malfunctioning of the older human’s subsystems or organs, such as delirium, syncope and falls in old age, which threaten the functioning of the older person as a whole. Either the person may recover from the perturbation induced by such a subsystem TP and the balance of the whole system is restored, or the TP may set in motion a cascade of events driving the system down to a state of more decline, ultimately leading to death. A main unanswered question here is how to predict whether these older persons will recover or not. To improve this TP-recovery-forecasting, intriguing findings on measures of resilience found in other complex biological systems may be translated to humans. New dynamic resilience biomarkers for resilience can enrich clinical prediction for pathophysiological recovery and could test interventions for their effectiveness in improving resilience. Therefore, we hypothesize that dynamic, stimulus-response measures of recovery rate over time, observed after having received a minor stressor in a healthy condition, can be used to quantify recovery potential following subsystem TPs in disease and following invasive treatments in humans and thus the person’s resilience. Current static frailty prognostics can predict risks for death, institutionalization, delirium, falls, and other TP transitions, but it has not been proven that they can predict recovery. Our hypothesis on dynamic indicators of recovery is logical and timely, as it can now be studied with sensor technology to create a fundamentally different approach of variables that may be validated to forecast recovery potential. By generating dynamic measures of systemic resilience over various organ systems, we may subsequently model resilience generically across many chronic diseases, affecting different organ systems. Next, quantifying systemic resilience may reroute scientific and clinical pathways by predicting and preventing irreversible tipping points and by improving recovery by older adults.

• forecasting
• frailty
• older adults
• resilience
• tipping points

• acute care
• advanced nursing
• municipality
• older persons
• phenomenological hermeneutic

Older patients is a complex group at increased risk of adverse outcomes compared to younger patients, which should be considered in the risk assessment performed in emergency departments. We evaluated whether the predictive ability of different risk assessment models for acutely admitted patients is affected by age.

Cohort study of middle-aged and older patients. We investigated the accuracy in discriminating between survivors and non-survivors within 7 days of different risk assessment models; a traditional triage algorithm, a triage algorithm with clinical assessment, vital signs, routine biomarkers, and the prognostic biomarker soluble urokinase plasminogen activator receptor (suPAR).

The cohort included 22,653 (53.2%) middle-aged patients (age 40–69 years), and 19,889 (46.8%) older patients (aged 70+ years). Death within 7 days occurred in 139 patients (0.6%) in middle-aged patients and 596 (3.0%) of the older patients. The models based on vital signs and routine biomarkers had the highest area under the curve (AUC), and both were significantly better at discriminating 7-day mortality in middle-aged patients compared to older patients; AUC (95% CI): 0.88 (0.84–0.91), 0.75 (0.72–0.78), P < 0.01, and 0.86 (0.82–0.90), 0.76 (0.73–0.78), P < 0.001. In a subgroup of the total cohort (6.400 patients, 15.0%), the suPAR level was available. suPAR had the highest AUC of all individual predictors with no significant difference between the age groups, but further research in this biomarker is required before it can be used.

The predictive value was lower in older patients compared to middle-aged patients for all investigated models. Vital signs or routine biomarkers constituted the best models for predicting 7-day mortality and were better than the traditional triage model. Hence, the current risk assessment for short-term mortality can be strengthened, but modifications for age should be considered when constructing new risk assessment models in the emergency department.

The online version of this article (10.1186/s12877-019-1154-7) contains supplementary material, which is available to authorized users.

• Emergency department
• Older patients
• Risk assessment
• Triage

Rapid response teams (RRTs) respond to hospitalized patients experiencing clinical deterioration and help determine subsequent management and disposition. We sought to evaluate and compare the prognostic accuracy of the Hamilton Early Warning Score (HEWS) and the National Early Warning Score 2 (NEWS2) for prediction of in-hospital mortality following RRT activation. We secondarily evaluated a subgroup of patients with suspected infection.

We retrospectively analyzed prospectively collected data (2012–2016) of consecutive RRT patients from two hospitals. The primary outcome was in-hospital mortality. We calculated the number needed to examine (NNE), which indicates the number of patients that need to be evaluated in order to detect one future death.

Five thousand four hundred ninety-one patients were included, of whom 1837 (33.5%) died in-hospital. Mean age was 67.4 years, and 51.6% were male. A HEWS above the low-risk threshold (≥ 5) had a sensitivity of 75.9% (95% confidence interval (CI) 73.9–77.9) and specificity of 67.6% (95% CI 66.1–69.1) for mortality, with a NNE of 1.84. A NEWS2 above the low-risk threshold (≥ 5) had a sensitivity of 84.5% (95% CI 82.8–86.2), and specificity of 49.0% (95% CI: 47.4–50.7), with a NNE of 2.20. The area under the receiver operating characteristic curve (AUROC) was 0.76 (95% CI 0.75–0.77) for HEWS and 0.72 (95% CI: 0.71–0.74) for NEWS2. Among suspected infection patients (n = 1708), AUROC for HEWS was 0.79 (95% CI 0.76–0.81) and for NEWS2, 0.75 (95% CI 0.73–0.78).

The HEWS has comparable clinical accuracy to NEWS2 for prediction of in-hospital mortality among RRT patients.

The online version of this article (10.1186/s13054-019-2355-3) contains supplementary material, which is available to authorized users.

• Early warning systems
• Intensive care unit
• Rapid response teams
• Sepsis

• ("emergency") and "triage"
• ("modified early warning score") or "mews"
• emergency department

• Adult
• Aged
• Area Under Curve
• China
• Critical Care
• Emergency Service, Hospital/organization & administration
• Female
• Hospital Mortality
• Hospitalization
• Humans
• Male
• Middle Aged
• Patient Discharge
• Prognosis
• Prospective Studies
• Sensitivity and Specificity
• Triage/methods

• Science and technology department of Guangdong province, China

• Hospital mortality
• National Early Warning Score
• Prognosis
• Rapid response system
• Screening

• Data analytics
• Deterioration detection
• Early warning scores
• Perioperative care

• Comorbidity
• Data Collection/methods
• Data Science
• Diagnosis, Computer-Assisted
• Humans
• Machine Learning
• Medical Informatics/methods
• Models, Theoretical
• Pattern Recognition, Automated
• Postoperative Complications/diagnosis
• Postoperative Period
• Risk Assessment

• Adult
• Aged
• Aged, 80 and over
• Analgesics, Opioid/adverse effects
• Female
• Heart Arrest/chemically induced
• Heart Arrest/epidemiology
• Hospital Mortality
• Hospitalization/statistics & numerical data
• Hospitals/statistics & numerical data
• Humans
• Hypnotics and Sedatives/adverse effects
• Male
• Middle Aged
• Respiratory Insufficiency/chemically induced
• Respiratory Insufficiency/epidemiology
• Retrospective Studies
• Risk Factors
• Surgery Department, Hospital/statistics & numerical data
• Young Adult

The reliability of wearable photoplethysmography (PPG) sensors to measure heart rate (HR) in hospitalized patients has only been demonstrated in adults. We evaluated the accuracy of HR monitoring with a personal fitness tracker (PFT) in children undergoing surgery. HR monitoring was performed using a wrist-worn PFT (Fitbit Charge HR) in 30 children (8.21 ± 3.09 years) undergoing laparoscopy (n = 8) or open surgery (n = 22). HR values were analyzed preoperatively and during surgery. The accuracy of HR recordings was compared with measurements recorded during continuous electrocardiographic (cECG) monitoring; HRs derived from continuous monitoring with pulse oximetry (SpO2R) were used as a positive control. PFT-derived HR values were in agreement with those recorded during cECG (r = 0.99) and SpO2R (r = 0.99) monitoring. PFT performance remained high in children < 8 years (r = 0.99), with a weight < 30 kg (r = 0.99) and when the HR was < 70 beats per minute (bpm) (r = 0.91) or > 140 bpm (r = 0.99). PFT accuracy was similar during laparoscopy and open surgery, as well as preoperatively and during the intervention (r > 0.9). PFT–derived HR showed excellent accuracy compared with HRs measured by cECG and SpO2R during pediatric surgical procedures. Further clinical evaluation is needed to define whether PFTs can be used in different health care settings.

• children
• heart rate
• pediatric surgery
• personal fitness tracker

• Coronary artery bypass grafting
• Elderly
• EuroSCORE II
• Risk evaluation system
• STS score
• SinoSCORE

Experimental cardiac arrest (CA) in aging research is infrequently studied in part due to the limitation of animal models. We aimed to develop an easily performed mouse CA model to meet this need. A standard mouse KCl-induced CA model using chest compressions and intravenous epinephrine for resuscitation was modified by blood withdrawal prior to CA onset, so as to decrease the requisite KCl dose to induce CA by decreasing the circulating blood volume. The modification was then compared to the standard model in young adult mice subjected to 8 min CA. 22-month old mice were then subjected to 8 min CA, resuscitated, and compared to young adult mice. Post-CA functional recovery was evaluated by measuring spontaneous locomotor activity pre-injury, and on post-CA days 1, 2, and 3. Neurological score and brain histology were examined on day 3. Brain elF2α phosphorylation levels were measured at 1 h to verify tissue stress. Compared to the standard model, the modification decreased cardiopulmonary resuscitation duration and increased 3-day survival in young mice. For aged mice, survival was 100 % at 24 h and 54% at 72 h. Neurological deficit was present 3 days post-CA, although more severe versus young mice. Mild neuronal necrosis was present in the cortex and hippocampus. The modified model markedly induced elF2α phosphorylation in both age groups. This modified procedure makes the CA model feasible in aged mice and provides a practical platform for understanding injury mechanisms and developing therapeutics for elderly patients.

• aging
• cardiac arrest
• mouse model
• resuscitation

• Critical care
• Heart rate monitoring
• Medical informatics
• Mobile health technologies
• Sleep quality
• Validation study
• Wearable devices

• Southeastern Ontario Academic Medical Organization

• Elderly
• Infection
• Physiology
• Sepsis

Due to atypical symptom presentation older patients are more prone to delayed sepsis recognition. We investigated whether initial disease severity before emergency department (ED) treatment (including treatable acute organ dysfunction), quality of ED sepsis care and the impact on mortality was different between patients older and younger than 70 years. If differences exist, improvements are needed for ED management of older patients at risk for sepsis.

In this observational multicenter study, ED patients who were hospitalized with a suspected infection were stratified by age <70 and ≥70 years. The presence of treatable and potentially reversible acute organ dysfunction was measured by the RO components of the Predisposition, Infection, Response and Organ dysfunction (PIRO) score, reflecting acute sepsis-related organ dysfunction developed before ED presentation. Quality of care, as assessed by the full compliance with nine quality performance measures and the standardized mortality ratio (SMR: observed/expected in-hospital mortality), was compared between older and younger patients.

The RO-components of the PIRO score were 8 (interquartile range; 4–9) in the 833 older patients, twice as high as the 4 (2–8; P<0.001) in the 1537 younger patients. However, full compliance with all nine quality performance measures was achieved in 34.2 (31.0–37.4)% of the older patients, not higher than the 33.0 (30.7–35.4)% in younger patients (P = 0.640). In-hospital mortality was 9.2% (95%-CI, 7.3–11.2) in patients ≥70, twice as high as the 4.6% (3.6–5.6) in patients <70 years, resulting in an SMR (in study period) of ~0.7 in both groups (P>0.05).

Older sepsis patients are sicker at ED presentation but are not treated more expediently or reliably despite their extra acuity The presence of twice as much treatable acute organ dysfunction before ED treatment suggests that acute organ dysfunction is recognized relatively late by general practitioners or patients in the out of hospital setting.

• Adult
• Age Factors
• Aged
• Aged, 80 and over
• Emergency Service, Hospital
• Female
• Hospital Mortality
• Humans
• Male
• Middle Aged
• Netherlands/epidemiology
• Organ Dysfunction Scores
• Outcome Assessment, Health Care
• Prospective Studies
• Quality Improvement
• Quality of Health Care
• Sepsis/diagnosis
• Sepsis/mortality
• Sepsis/therapy
• Severity of Illness Index