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This study has two main objectives: to describe the prevalence of undetected chronic obstructive pulmonary disease (COPD) in a clinical sample of smokers with severe mental illness (SMI), and to assess the value of the Tobacco Intensive Motivational Estimated Risk tool, which informs smokers of their respiratory risk and uses brief text messages to reinforce intervention.
A multicenter, randomized, open-label, and active-controlled clinical trial, with a 12-month follow-up. Outpatients with schizophrenia (SZ) and bipolar disorder were randomized either to the experimental group—studied by spirometry and informed of their calculated lung age and degree of obstruction (if any)—or to the active control group, who followed the 5 A’s intervention.
The study sample consisted of 160 patients (71.9% SZ), 78.1% of whom completed the 12-month follow-up. Of the patients who completed the spirometry test, 23.9% showed evidence of COPD (77.8% in moderate or severe stages). TIMER was associated with a significant reduction in tobacco use at week 12 and in the long term, 21.9% of patients reduced consumption and 14.6% at least halved it. At week 48, six patients (7.3%) allocated to the experimental group achieved the seven-day smoking abstinence confirmed by CO (primary outcome in terms of efficacy), compared to three (3.8%) in the control group.
In this clinical pilot trial, one in four outpatients with an SMI who smoked had undiagnosed COPD. An intensive intervention tool favors the early detection of COPD and maintains its efficacy to quit smoking, compared with the standard 5 A’s intervention.
Individuals with schizophrenia are at higher risk of physical illnesses, which are a major contributor to their 20-year reduced life expectancy. It is currently unknown what causes the increased risk of physical illness in schizophrenia.
To link genetic data from a clinically ascertained sample of individuals with schizophrenia to anonymised National Health Service (NHS) records. To assess (a) rates of physical illness in those with schizophrenia, and (b) whether physical illness in schizophrenia is associated with genetic liability.
We linked genetic data from a clinically ascertained sample of individuals with schizophrenia (Cardiff Cognition in Schizophrenia participants, n = 896) to anonymised NHS records held in the Secure Anonymised Information Linkage (SAIL) databank. Physical illnesses were defined from the General Practice Database and Patient Episode Database for Wales. Genetic liability for schizophrenia was indexed by (a) rare copy number variants (CNVs), and (b) polygenic risk scores.
Individuals with schizophrenia in SAIL had increased rates of epilepsy (standardised rate ratio (SRR) = 5.34), intellectual disability (SRR = 3.11), type 2 diabetes (SRR = 2.45), congenital disorders (SRR = 1.77), ischaemic heart disease (SRR = 1.57) and smoking (SRR = 1.44) in comparison with the general SAIL population. In those with schizophrenia, carrier status for schizophrenia-associated CNVs and neurodevelopmental disorder-associated CNVs was associated with height (P = 0.015–0.017), with carriers being 7.5–7.7 cm shorter than non-carriers. We did not find evidence that the increased rates of poor physical health outcomes in schizophrenia were associated with genetic liability for the disorder.
This study demonstrates the value of and potential for linking genetic data from clinically ascertained research studies to anonymised health records. The increased risk for physical illness in schizophrenia is not caused by genetic liability for the disorder.
Diagnosis of schizophrenia spectrum disorders (SSD) may be difficult in clinical practice, particularly during the first episodes of early-onset psychosis (FE-EOP).
To develop a Support Vector Machine (SVM) algorithm as a predictive tool for diagnostic outcome in patients with FE-EOP, based on clinical and biomedical data at the emergence of the illness.
Two-year, prospective longitudinal study, where 81 patients (9-17 years of age) with a FE-EOP and stable diagnosis at follow-up and 41 age and sex-matched healthy controls (HC) were included. Structured diagnostic interviews, clinical and cognitive scales, a MRI scan and biochemical tests were conducted at baseline. Three SVM classification algorithms were developed (SSD vs HC group, non-SSD vs HC group, and SSD vs non-SSD group). Jackknifing was used to validate the algorithms and to calculate performance estimates. Enhanced-Recursive Feature Elimination was performed in order to gain information about the predictive weight for diagnosis of each variable.
The SSD-versus-non-SSD classifier achieved an overall accuracy of 83.1%, sensitivity of 86.6% and specificity of 77.8%. The variables during a FE-EOP with higher predictive value for a diagnosis of SSD were clinical variables such as negative symptoms preceding or during the psychotic onset, poor insight and duration of illness until first psychiatric contact. Biochemical, neuroimaging, and cognitive variables at baseline did not provide any additional predictive value.
SVM may serve as a predictive tool for early diagnosis of SSD during a FE-EOP. The most discriminative variables during a FE-EOP for a future diagnosis of SSD are clinical variables.
In cases of mass-casualty incidents (MCIs), triage represents a fundamental tool for the management of and assistance to the wounded, which helps discriminate not only the priority of attention, but also the priority of referral to the most suitable center.
The objective of this study was to evaluate the capacity of different prehospital triage systems based on physiological parameters (Shock Index [SI], Glasgow-Age-Pressure Score [GAP], Revised Trauma Score [RTS], and National Early Warning Score 2 [NEWS2]) to predict early mortality (within 48 hours) from the index event for use in MCIs.
This was a longitudinal prospective observational multi-center study on patients who were attended by Advanced Life Support (ALS) units and transferred to the emergency department (ED) of their reference hospital. Collected were: demographic, physiological, and clinical variables; main diagnosis; and data on early mortality. The main outcome variable was mortality from any cause within 48 hours.
From April 1, 2018 through February 28, 2019, a total of 1,288 patients were included in this study. Of these, 262 (20.3%) participants required assistance for trauma and injuries by external agents. Early mortality within the first 48 hours due to any cause affected 69 patients (5.4%). The system with the best predictive capacity was the NEWS2 with an area under the curve (AUC) of 0.891 (95% CI, 0.84-0.94); a sensitivity of 79.7% (95% CI, 68.8-87.5); and a specificity of 84.5% (95% CI, 82.4-86.4) for a cut-off point of nine points, with a positive likelihood ratio of 5.14 (95% CI, 4.31-6.14) and a negative predictive value of 98.7% (95% CI, 97.8-99.2).
Prehospital scores of the NEWS2 are easy to obtain and represent a reliable test, which make it an ideal system to help in the initial assessment of high-risk patients, and to determine their level of triage effectively and efficiently. The Prehospital Emergency Medical System (PhEMS) should evaluate the inclusion of the NEWS2 as a triage system, which is especially useful for the second triage (evacuation priority).
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