Positive symptoms are a useful predictor of aggression in schizophrenia. Although a similar pattern of abnormal brain structures related to both positive symptoms and aggression has been reported, this observation has not yet been confirmed in a single sample.

To study the association between positive symptoms and aggression in schizophrenia on a neurobiological level, a prospective meta-analytic approach was employed to analyze harmonized structural neuroimaging data from 10 research centers worldwide. We analyzed brain MRI scans from 902 individuals with a primary diagnosis of schizophrenia and 952 healthy controls.

The result identified a widespread cortical thickness reduction in schizophrenia compared to their controls. Two separate meta-regression analyses revealed that a common pattern of reduced cortical gray matter thickness within the left lateral temporal lobe and right midcingulate cortex was significantly associated with both positive symptoms and aggression.

These findings suggested that positive symptoms such as formal thought disorder and auditory misperception, combined with cognitive impairments reflecting difficulties in deploying an adaptive control toward perceived threats, could escalate the likelihood of aggression in schizophrenia.

In the intensive care setting, delirium is a common occurrence; however, the impact of the level of alertness has never been evaluated. Therefore, this study aimed to assess the delirium characteristics in the drowsy, as well as the alert and calm patient.

In this prospective cohort study, 225 intensive care patients with Richmond Agitation and Sedation Scale (RASS) scores of −1 — drowsy and 0 — alert and calm were evaluated with the Delirium Rating Scale-Revised-1998 (DRS-R-98) and the Diagnostic and Statistical Manual 4th edition text revision (DSM-IV-TR)-determined diagnosis of delirium.

In total, 85 drowsy and 140 alert and calm patients were included. Crucial items for the correct identification of delirium were sleep–wake cycle disturbances, language abnormalities, thought process alterations, psychomotor retardation, disorientation, inattention, short- and long-term memory, as well as visuo-spatial impairment, and the temporal onset. Conversely, perceptual disturbances, delusions, affective lability, psychomotor agitation, or fluctuations were items, which identified delirium less correctly. Further, the severities of inattentiveness and visuo-spatial impairment were indicative of delirium in both alert- or calmness and drowsiness.

The impairment in the cognitive domain, psychomotor retardation, and sleep–wake cycle disturbances correctly identified delirium irrespective of the level alertness. Further, inattentiveness and — to a lesser degree — visuo-spatial impairment could represent a specific marker for delirium in the intensive care setting meriting further evaluation.

The importance of the proper identification of delirium, with its high incidence and adversities in the intensive care setting, has been widely recognized. One common screening instrument is the Intensive Care Delirium Screening Checklist (ICDSC); however, the symptom profile and key features of delirium dependent on the level of sedation have not yet been evaluated.

In this prospective cohort study, the ICDSC was evaluated versus the Diagnostic and Statistical Manual, 4th edition, text revision, diagnosis of delirium set as standard with respect to the symptom profile, and correct identification of delirium. The aim of this study was to identify key features of delirium in the intensive care setting dependent on the Richmond Agitation and Sedation Scale levels of sedation: drowsiness versus alert and calmness.

The 88 delirious patients of 225 were older, had more severe disease, and prolonged hospitalization. Irrespective of the level of sedation, delirium was correctly classified by items related to inattention, disorientation, psychomotor alterations, inappropriate speech or mood, and symptom fluctuation. In the drowsy patients, inattention reached substantial sensitivity and specificity, whereas psychomotor alterations and sleep-wake cycle disturbances were sensitive lacked specificity. The positive prediction was substantial across items, whereas the negative prediction was only moderate. In the alert and calm patient, the sensitivities were substantial for psychomotor alterations, sleep-wake cycle disturbances, and symptom fluctuations; however, these fluctuations were not specific. The positive prediction was moderate and the negative prediction substantial. Between the nondelirious drowsy and alert, the symptom profile was similar; however, drowsiness was associated with alterations in consciousness.

In the clinical routine, irrespective of the level of sedation, delirium was characterized by the ICDSC items for inattention, disorientation, psychomotor alterations, inappropriate speech or mood and symptom fluctuation. Further, drowsiness caused altered levels of consciousness.

Similar to delirium, its subsyndromal form has been recognized as the cause of diverse adverse outcomes. Nonetheless, the nature of this subsyndromal delirium remains vastly understudied. Therefore, in the following, we evaluate the phenomenological characteristics of this syndrome versus no and full-syndromal delirium.

In this prospective cohort study, we evaluated the Delirium Rating Scale–Revised, 1998 (DRS–R–98) versus the Diagnostic and Statistical Manual of Mental Disorders, 4th ed., Text Revision (DSM–IV–TR) diagnostic criteria and examined the diagnosis of delirium with respect to phenomenological distinctions in the intensive care setting.

Out of 289 patients, 36 with subsyndromal delirium versus 86 with full-syndromal and 167 without delirium were identified. Agreement with respect to the DSM–IV–TR diagnosis of delirium was perfect. The most common subtype in those with subsyndromal delirium was hypoactive, in contrast to mixed subtype in those with full-syndromal delirium versus no motor alterations in those without delirium. By presence and severity of delirium symptoms, subsyndromal delirium was intermediate. The ability of the DRS–R–98 items to discriminate between either form of delirium was substantial. Between subsyndromal and no delirium, the cognitive domain and sleep–wake cycle were more impaired and allowed a distinction with no delirium. Further, between full- and subsyndromal delirium, the prevalence and severity of individual DRS–R–98 items were greater. Although the differences between these two forms of delirium was substantial, the items were not very specific, indicating that the phenomenology of subsyndromal delirium is closer to full-syndromal delirium.

Phenomenologically, subsyndromal delirium was found to be distinct from and intermediate between no delirium and full-syndromal delirium. Moreover, the greater proximity to full-syndromal delirium indicated that subsyndromal delirium represents an identifiable subform of full-syndromal delirium.

In the intensive care setting, delirium is a common occurrence that comes with subsequent adversities. Therefore, several instruments have been developed to screen for and detect delirium. Their validity and psychometric properties, however, remain controversial.

In this prospective cohort study, the Confusion Assessment Method for the Intensive Care Unit (CAM–ICU) and the Intensive Care Delirium Screening Checklist (ICDSC) were evaluated versus the DSM–IV–TR in the diagnosis of delirium with respect to their validity and psychometric properties.

Out of some 289 patients, 210 with matching CAM–ICU, ICDSC, and DSM–IV–TR diagnoses were included. Between the scales, the prevalence of delirium ranged from 23.3% with the CAM–ICU, to 30.5% with the ICDSC, to 43.8% with the DSM–IV–TR criteria. The CAM–ICU showed only moderate concurrent validity (Cohen's κ = 0.44) and sensitivity (50%), but high specificity (95%). The ICDSC also reached moderate agreement (Cohen's κ = 0.60) and sensitivity (63%) while being very specific (95%). Between the CAM–ICU and the ICDSC, the concurrent validity was again only moderate (Cohen's κ = 0.56); however, the ICDSC yielded higher sensitivity and specificity (78 and 83%, respectively).

In the daily clinical routine, neither the CAM–ICU nor the ICDSC, common tools used in screening and detecting delirium in the intensive care setting, reached sufficient concurrent validity; nor did they outperform the DSM–IV–TR diagnostic criteria with respect to sensitivity or positive prediction, but they were very specific. Thus, the non-prediction by the CAM–ICU or ICDSC did not refute the presence of delirium. Between the CAM–ICU and ICDSC, the ICDSC proved to be the more accurate instrument.

The management of and prognosis for delirium are affected by its subtype: hypoactive, hyperactive, mixed, and none. The DMSS–4, an abbreviated version of the Delirium Motor Symptom Scale, is a brief instrument for the assessment of delirium subtypes. However, it has not yet been evaluated in an intensive care setting.

We performed a prospective/descriptive cohort study in order to determine the internal consistency, reliability, and validity of the relevant items of the DMSS–4 versus the Delirium Rating Scale–Revised-98 (DRS–R-98) and the original DMSS in a surgical intensive care setting.

A total of 289 elderly, predominantly male patients were screened for delirium, and 122 were included in our sample. The internal consistency of the DMSS–4 items was excellent (Cronbach's α = 0.92), and between the DMSS–4 and DRS–R-98 the overall concurrent validity was substantial (Cramer's V = 0.67). Within individual motor subtypes, concurrent validity remained at least substantial (Cohen's κ = 0.65–0.81) and sensitivity high (69.8 to 82.2%), in contrast to those of the no-motor subtype, with less validity and sensitivity (κ = 0.28, 22%). Similarly, total concurrent validity between the DMSS–4 and the original DMSS reached perfection (Cramer's V = 0.83), as did agreement between the subtypes (κ = 0.83–0.92), while sensitivity remained high (88.2–100%). Only in those with delirium with no-motor subtype was agreement moderate (κ = 0.56) and sensitivity lower (67%). Specificity was high across all subtypes (91.2–99.1%). The DMSS–4 yielded very sensitive ratings, particularly for hypoactive and hyperactive motor symptoms, and interrater agreement was excellent (Fleiss's κ = 0.83).

We found the DMSS–4 to be a most reliable and valid brief assessment of delirium in characterizing the subtypes of delirium in an intensive care setting, with increased sensitivity to hypoactive and hyperactive motor alterations.

To describe the impact of rapid diagnostic microarray technology and antimicrobial stewardship for patients with Gram-positive blood cultures.

Retrospective pre-intervention/post-intervention study.

A 1,200-bed academic medical center.

Inpatients with blood cultures positive for Staphylococcus aureus, Enterococcus faecalis, E. faecium, Streptococcus pneumoniae, S. pyogenes, S. agalactiae, S. anginosus, Streptococcus spp., and Listeria monocytogenes during the 6 months before and after implementation of Verigene Gram-positive blood culture microarray (BC-GP) with an antimicrobial stewardship intervention.

Before the intervention, no rapid diagnostic technology was used or antimicrobial stewardship intervention was undertaken, except for the use of peptide nucleic acid fluorescent in situ hybridization and MRSA agar to identify staphylococcal isolates. After the intervention, all Gram-positive blood cultures underwent BC-GP microarray and the antimicrobial stewardship intervention consisting of real-time notification and pharmacist review.

In total, 513 patients with bacteremia were included in this study: 280 patients with S. aureus, 150 patients with enterococci, 82 patients with stretococci, and 1 patient with L. monocytogenes. The number of antimicrobial switches was similar in the pre–BC-GP (52%; 155 of 300) and post–BC-GP (50%; 107 of 213) periods. The time to antimicrobial switch was significantly shorter in the post–BC-GP group than in the pre–BC-GP group: 48±41 hours versus 75±46 hours, respectively (P<.001). The most common antimicrobial switch was de-escalation and time to de-escalation, was significantly shorter in the post-BC-GP group than in the pre–BC-GP group: 53±41 hours versus 82±48 hours, respectively (P<.001). There was no difference in mortality or hospital length of stay as a result of the intervention.

The combination of a rapid microarray diagnostic test with an antimicrobial stewardship intervention improved time to antimicrobial switch, especially time to de-escalation to optimal therapy, in patients with Gram-positive blood cultures.

Infect Control Hosp Epidemiol 2016;1–6

Various transmission routes contribute to spread of carbapenem-resistant Klebsiella pneumoniae (CRKP) in hospitalized patients. Patients with readmissions during which CRKP is again isolated (“CRKP readmission”) potentially contribute to transmission of CRKP.

To evaluate CRKP readmissions in the Consortium on Resistance against Carbapenems in K. pneumoniae (CRaCKLe).

Cohort study from December 24, 2011, through July 1, 2013.

Multicenter consortium of acute care hospitals in the Great Lakes region.

All patients who were discharged alive during the study period were included. Each patient was included only once at the time of the first CRKP-positive culture.

All readmissions within 90 days of discharge from the index hospitalization during which CRKP was again found were analyzed. Risk factors for CRKP readmission were evaluated in multivariable models.

Fifty-six (20%) of 287 patients who were discharged alive had a CRKP readmission. History of malignancy was associated with CRKP readmission (adjusted odds ratio [adjusted OR], 3.00 [95% CI, 1.32–6.65], P<.01). During the index hospitalization, 160 patients (56%) received antibiotic treatment against CRKP; the choice of regimen was associated with CRKP readmission (P=.02). Receipt of tigecycline-based therapy (adjusted OR, 5.13 [95% CI, 1.72–17.44], using aminoglycoside-based therapy as a reference in those treated with anti-CRKP antibiotics) was associated with CRKP readmission.

Hospitalized patients with CRKP—specifically those with a history of malignancy—are at high risk of readmission with recurrent CRKP infection or colonization. Treatment during the index hospitalization with a tigecycline-based regimen increases this risk.

Infect. Control Hosp. Epidemiol. 2016;37(3):281–288

To determine the rates of and risk factors for tigecycline nonsusceptibility among carbapenem-resistant Klebsiella pneumoniae (CRKPs) isolated from hospitalized patients

Multicenter prospective observational study

Acute care hospitals participating in the Consortium on Resistance against Carbapenems in Klebsiella pneumoniae (CRaCKle)

A cohort of 287 patients who had CRKPs isolated from clinical cultures during hospitalization

For the period from December 24, 2011 to October 1, 2013, the first hospitalization of each patient with a CRKP during which tigecycline susceptibility for the CRKP isolate was determined was included. Clinical data were entered into a centralized database, including data regarding pre-hospital origin. Breakpoints established by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) were used to interpret tigecycline susceptibility testing.

Of 287 patients included in the final cohort, 155 (54%) had tigecycline-susceptible CRKPs. Of all index isolates, 81 (28%) were tigecycline-intermediate and 51 (18%) were tigecycline resistant. In multivariate modeling, independent risk factors for tigecycline nonsusceptibility were (1) admission from a skilled nursing facility (OR, 2.51; 95% CI, 1.51–4.21; P=.0004), (2) positive culture within 2 days of admission (OR, 1.82; 95% CI, 1.06–3.15; P=.03), and (3) receipt of tigecycline within 14 days (OR, 4.38, 95% CI, 1.37–17.01, P=.02).

In hospitalized patients with CRKPs, tigecycline nonsusceptibility was more frequently observed in those admitted from skilled nursing facilities and occurred earlier during hospitalization. Skilled nursing facilities are an important target for interventions to decrease antibacterial resistance to antibiotics of last resort for treatment of CRKPs.

Infect Control Hosp Epidemiol 2015;36(8):942–948