Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

To describe and compare the mortality associated with nosocomial pneumonia due to Pseudomonas aeruginosa (Pa-NP) according to pneumonia classification (community-onset pneumonia [COP], hospital-acquired pneumonia [(HAP], and ventilator-associated pneumonia [VAP]).

We conducted a retrospective cohort study of adults with Pa-NP. We compared mortality for Pa-NP among patients with COP, HAP, and VAP and used logistic regression to identify risk factors for hospital mortality and inappropriate initial antibiotic therapy (IIAT).

Twelve acute care hospitals in 5 countries (United States, 3; France, 2; Germany, 2; Italy, 2; and Spain, 3).

A total of 742 patients with Pa-NP.

Hospital mortality was greater for those with VAP (41.9%) and HAP (40.1%) compared with COP (24.5%) (P<.001). In multivariate analyses, independent predictors of hospital mortality differed by pneumonia classification (COP: need for mechanical ventilation and intensive care; HAP: multidrug-resistant isolate; VAP: IIAT, increasing age, increasing Charlson comorbidity score, bacteremia, and use of vasopressors). Presence of multidrug resistance was identified as an independent predictor of IIAT for patients with COP and HAP, whereas recent antibiotic administration was protective in patients with VAP.

Among patients with Pa-NP, pneumonia classification identified patients with different risks for hospital mortality. Specific risk factors for hospital mortality also differed by pneumonia classification and multidrug resistance appeared to be an important risk factor for IIAT. These findings suggest that pneumonia classification for P. aeruginosa identifies patients with different mortality risks and specific risk factors for outcome and IIAT.

Infect Control Hosp Epidemiol 2015;36(10):1190–1197

We aimed to use the consensus opinion of a group of expert emergency physicians to derive a set of emergency diagnoses for acute abdominal pain that might be used as clinically significant outcomes for future research.

We conducted a cross-sectional survey of a convenience sample of emergency physicians with expertise in abdominal pain. These experts were authors of textbook chapters, peer-reviewed original research with a focus on abdominal pain or widely published clinical guidelines. Respondents were asked to categorize 50 possible diagnoses of acute abdominal pain into 1 of 3 categories: 1) unacceptable not to diagnose on the first emergency department (ED) visit; 2) although optimal to diagnose on first visit, failure to diagnose would not be expected to have serious adverse consequences provided the patient had follow-up within the next 2–7 days; 3) if not diagnosed during the first visit, unlikely to cause long-term risk to the patient provided the patient had follow-up within the next 1–2 months. Standard descriptive statistical analysis was used to summarize survey data.

Thirty emergency physicians completed the survey. Of 50 total diagnoses, 16 were categorized as “unacceptable not to diagnose in the ED” with greater than 85% agreement, and 12 were categorized as “acceptable not to diagnose in the ED” with greater than 85% agreement.

Our study identifies a set of abdominal pain conditions considered by expert emergency physicians to be clinically important to diagnose during the initial ED visit. These diseases may be used as “clinically significant” outcomes for future research on abdominal pain.