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In hospitals in England, patients’ vital signs are monitored and summarized into the National Early Warning Score (NEWS); this score is more accurate than the Quick Sepsis-related Organ Failure Assessment (qSOFA) score at identifying patients with sepsis. We investigated the extent to which the accuracy of the NEWS is enhanced by developing and comparing 3 computer-aided NEWS (cNEWS) models (M0 = NEWS alone, M1 = M0 + age + sex, M2 = M1 + subcomponents of NEWS + diastolic blood pressure) to predict the risk of sepsis. We included all emergency medical admissions of patients 16 years of age and older discharged over 24 months from 2 acute care hospital centres (York Hospital [YH] for model development and a combined data set from 2 hospitals [Diana, Princess of Wales Hospital and Scunthorpe General Hospital] in the Northern Lincolnshire and Goole National Health Service Foundation Trust [NH] for external model validation). We used a validated Canadian method for defining sepsis from administrative hospital data. The prevalence of sepsis was lower in YH (4.5%, 1596/35 807) than in NH (8.5%, 2983/35 161). The C statistic increased across models (YH: M0 0.705, M1 0.763, M2 0.777; NH: M0 0.708, M1 0.777, M2 0.791). For NEWS of 5 or higher, sensitivity increased (YH: 47.24% v. 50.56% v. 52.69%; NH: 37.91% v. 43.35% v. 48.07%), the positive likelihood ratio increased (YH: 2.77 v. 2.99 v. 3.06; NH: 3.18 v. 3.32 v. 3.45) and the positive predictive value increased (YH: 11.44% v. 12.24% v. 12.49%; NH: 22.75% v. 23.55% v. 24.21%). From the 3 cNEWS models, model M2 is the most accurate. Given that it places no additional burden of data collection on clinicians and can be automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

ObjectivesIn the English National Health Service, the patient’s vital signs are monitored and summarised into a National Early Warning Score (NEWS) to support clinical decision making, but it does not provide an estimate of the patient’s risk of death. We examine the extent to which the accuracy of NEWS for predicting mortality could be improved by enhanced computer versions of NEWS (cNEWS).DesignLogistic regression model development and external validation study.SettingTwo acute hospitals (YH—York Hospital for model development; NH—Northern Lincolnshire and Goole Hospital for external model validation).ParticipantsAdult (≥16 years) medical admissions discharged over a 24-month period with electronic NEWS (eNEWS) recorded on admission are used to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) using the first electronically recorded NEWS (model M0) versus a cNEWS model which included age+sex (model M1) +subcomponents of NEWS (including diastolic blood pressure) (model M2).ResultsThe risk of dying in-hospital following emergency medical admission was 5.8% (YH: 2080/35 807) and 5.4% (NH: 1900/35 161). The c-statistics for model M2 in YH for predicting mortality (in-hospital=0.82, 24 hours=0.91, 48 hours=0.88 and 72 hours=0.88) was higher than model M0 (in-hospital=0.74, 24 hours=0.89, 48 hours=0.86 and 72 hours=0.85) with higher Positive Predictive Value (PPVs) for in-hospital mortality (M2 19.3% and M0 16.6%). Similar findings were seen in NH. Model M2 performed better than M0 in almost all major disease subgroups.ConclusionsAn externally validated enhanced computer-aided NEWS model (cNEWS) incrementally improves on the performance of a NEWS only model. Since cNEWS places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated to determine if it can improve care in hospitals that have eNEWS systems.

BACKGROUND: In hospitals in England, patients' vital signs are monitored and summarized into the National Early Warning Score (NEWS); this score is more accurate than the Quick Sepsis related Organ Failure Assessment (qSOFA) score at identifying patients with sepsis. We investigated the extent to which the accuracy of the NEWS is enhanced by developing and comparing 3 computer-aided NEWS (cNEWS) models (M0 = NEWS alone, Ml = M0 + age + sex, M2 = Ml + subcomponents of NEWS + diastolic blood pressure) to predict the risk of sepsis. METHODS: We included all emergency medical admissions of patients 16 years of age and older discharged over 24 months from 2 acute care hospital centres (York Hospital [YH] for model development and a combined data set from 2 hospitals [Diana, Princess of Wales Hospital and Scunthorpe General Hospital] in the Northern Lincolnshire and Goole National Health Service Foundation Trust [NH] for external model validation). We used a validated Canadian method for defining sepsis from administrative hospital data. RESULTS: The prevalence of sepsis was lower in YH (4.5%, 1596/35807) than in NH (8.5%, 2983/35161). The C statistic increased across models (YH: M0 0.705, M1 0.763, M2 0.777; NH: M0 0.708, M1 0.777, M2 0.791). For NEWS of 5 or higher, sensitivity increased (YH: 47.24% v. 50.56% v. 52.69%; NH: 37.91% v. 43.35% v. 48.07%), the positive likelihood ratio increased (YH: 2.77 v. 2.99 v. 3.06; NH: 3.18 v. 3.32 v. 3.45) and the positive predictive value increased (YH: 11.44% v. 12.24% v. 12.49%; NH: 22.75% v. 23.55% v. 24.21%). INTERPRETATION: From the 3 cNEWS models, model M2 is the most accurate. Given that it places no additional burden of data collection on clinicians and can be automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

Objective Routine administrative data have been used to show that patients admitted to hospitals over the weekend appear to have a higher mortality compared to weekday admissions. Such data do not take the severity of sickness of a patient on admission into account. Our aim was to incorporate a standardized vital signs physiological-based measure of sickness known as the National Early Warning Score to investigate if weekend admissions are: sicker as measured by their index National Early Warning Score; have an increased mortality; and experience longer delays in the recording of their index National Early Warning Score. Methods We extracted details of all adult emergency medical admissions during 2014 from hospital databases and linked these with electronic National Early Warning Score data in four acute hospitals. We analysed 47,117 emergency admissions after excluding 1657 records, where National Early Warning Score was missing or the first (index) National Early Warning Score was recorded outside ±24 h of the admission time. Results Emergency medical admissions at the weekend had higher index National Early Warning Score (weekend: 2.53 vs. weekday: 2.30, p < 0.001) with a higher mortality (weekend: 706/11,332 6.23% vs. weekday: 2039/35,785 5.70%; odds ratio = 1.10, 95% CI 1.01 to 1.20, p = 0.04) which was no longer seen after adjusting for the index National Early Warning Score (odds ratio = 0.99, 95% CI 0.90 to 1.09, p = 0.87). Index National Early Warning Score was recorded sooner (−0.45 h, 95% CI −0.52 to −0.38, p < 0.001) for weekend admissions. Conclusions Emergency medical admissions at the weekend with electronic National Early Warning Score recorded within 24 h are sicker, have earlier clinical assessments, and after adjusting for the severity of their sickness, do not appear to have a higher mortality compared to weekday admissions. A larger definitive study to confirm these findings is needed.

The National Early Warning Score (NEWS) is being replaced with NEWS2 which adds 3 points for new confusion or delirium. We estimated the impact of adding delirium on the number of medium/high level alerts that are triggers to escalate care. Analysis of emergency medical admissions in two acute hospitals (York Hospital (YH) and Northern Lincolnshire and Goole NHS Foundation Trust hospitals (NH)) in England. Twenty per cent were randomly assigned to have delirium. The number of emergency admissions (YH: 35584; NH: 35795), mortality (YH: 5.7%; NH: 5.5%), index NEWS (YH: 2.5; NH: 2.1) and numbers of NEWS recorded (YH: 879193; NH: 884072) were similar in each hospital. The mean number of patients with medium level alerts per day increased from 55.3 (NEWS) to 69.5 (NEWS2), a 25.7% increase in YH and 64.1 (NEWS) to 77.4 (NEWS2), a 20.7% increase in NH. The mean number of patients with high level alerts per day increased from 27.3 (NEWS) to 34.4 (NEWS2), a 26.0% increase in YH and 29.9 (NEWS) to 37.7 (NEWS2), a 26.1% increase in NH. The addition of delirium in NEWS2 will have a substantial increase in medium and high level alerts in hospitalised emergency medical patients. Rigorous evaluation of NEWS2 is required before widespread implementation because the extent to which staff can cope with this increase without adverse consequences remains unknown.

ObjectivesThere are no established mortality risk equations specifically for emergency medical patients who are admitted to a general hospital ward. Such risk equations may be useful in supporting the clinical decision-making process. We aim to develop and externally validate a computer-aided risk of mortality (CARM) score by combining the first electronically recorded vital signs and blood test results for emergency medical admissions.DesignLogistic regression model development and external validation study.SettingTwo acute hospitals (Northern Lincolnshire and Goole NHS Foundation Trust Hospital (NH)—model development data; York Hospital (YH)—external validation data).ParticipantsAdult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic National Early Warning Score(s) and blood test results recorded on admission.ResultsThe risk of in-hospital mortality following emergency medical admission was 5.7% (NH: 1766/30 996) and 6.5% (YH: 1703/26 247). The C-statistic for the CARM score in NH was 0.87 (95% CI 0.86 to 0.88) and was similar in an external hospital setting YH (0.86, 95% CI 0.85 to 0.87) and the calibration slope included 1 (0.97, 95% CI 0.94 to 1.00).ConclusionsWe have developed a novel, externally validated CARM score with good performance characteristics for estimating the risk of in-hospital mortality following an emergency medical admission using the patient’s first, electronically recorded, vital signs and blood test results. Since the CARM score places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

The day-old Holstein calf wobbled to her feet as Lt. Cmdr. Pete Peterson entered the barn. The newest addition to the U.S. Naval Academy Dairy's herd of 428 cows looked up at the retired naval supply corps officer and attempted to nuzzle his fingers.

Schizophrenia remains a major burden on patients and society. The dopamine hypothesis attempts to explain the pathogenic mechanisms of the disorder, and the neurodevelopmental hypothesis the origins. In the past 10 years an alternative, the cognitive model, has gained popularity. However, the first two theories have not been satisfactorily integrated, and the most influential iteration of the cognitive model makes no mention of dopamine, neurodevelopment, or indeed the brain. In this Review we show that developmental alterations secondary to variant genes, early hazards to the brain, and childhood adversity sensitise the dopamine system, and result in excessive presynaptic dopamine synthesis and release. Social adversity biases the cognitive schema that the individual uses to interpret experiences towards paranoid interpretations. Subsequent stress results in dysregulated dopamine release, causing the misattribution of salience to stimuli, which are then misinterpreted by the biased cognitive processes. The resulting paranoia and hallucinations in turn cause further stress, and eventually repeated dopamine dysregulation hardwires the psychotic beliefs. Finally, we consider the implications of this model for understanding and treatment of schizophrenia.

Signalling at dopamine D2/D3 receptors is thought to underlie motivated behaviour, pleasure experiences and emotional expression based on animal studies, but it is unclear if this is the case in humans or how this relates to neural processing of reward stimuli. Using a randomised, double-blind, placebo-controlled, crossover neuroimaging study, we show in healthy humans that sustained dopamine D2/D3 receptor antagonism for 7 days results in negative symptoms (impairments in motivated behaviour, hedonic experience, verbal and emotional expression) and that this is related to blunted striatal response to reward stimuli. In contrast, 7 days of partial D2/D3 agonism does not disrupt reward signalling, motivated behaviour or hedonic experience. Both D2/D3 antagonism and partial agonism induce motor impairments, which are not related to striatal reward response. These findings identify a central role for D2/D3 signalling and reward processing in the mechanism underlying motivated behaviour and emotional responses in humans, with implications for understanding neuropsychiatric disorders such as schizophrenia and Parkinson’s disease. Osugo et al show in healthy humans that sustained dopamine D2/D3 receptor antagonism impairs motivated behaviour, hedonic experience, and emotional expression, and that this is related to blunted striatal reward response following D2/D3 antagonism.

Alterations in brain glutamate levels may be associated with psychosis risk, but the relationship to clinical outcome in at-risk individuals is unknown. Glutamate concentration was measured in the left thalamus and anterior cingulate cortex (ACC) using 3-Tesla proton magnetic resonance spectroscopy in 75 participants at ultra high risk (UHR) of psychosis and 56 healthy controls. The severity of attenuated positive symptoms and overall functioning were assessed. Measures were repeated in 51 UHR and 33 Control subjects after a mean of 18 months. UHR subjects were allocated to either remission (no longer meeting UHR criteria) or non-remission (meeting UHR or psychosis criteria) status on follow-up assessment. Thalamic glutamate levels at presentation were lower in the UHR non-remission (N=29) compared with the remission group (N=22) (t(49)=3.03; P=0.004), and were associated with an increase in the severity of total positive symptoms over time (r=-0.33; df=47; P=0.02), most notably abnormal thought content (r=-0.442; df=47; P=0.003). In the UHR group, ACC glutamate levels were lower at follow-up compared with baseline (F(80)=4.28; P=0.04). These findings suggest that measures of brain glutamate function may be useful as predictors of clinical outcome in individuals at high risk of psychosis.