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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.
Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.
To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.
Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.
Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.
AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.
Quasi-experimental studies evaluate the association between an intervention and an outcome using experiments in which the intervention is not randomly assigned. Quasi-experimental studies are often used to evaluate rapid responses to outbreaks or other patient safety problems requiring prompt, nonrandomized interventions. Quasi-experimental studies can be categorized into 3 major types: interrupted time-series designs, designs with control groups, and designs without control groups. This methods paper highlights key considerations for quasi-experimental studies in healthcare epidemiology and antimicrobial stewardship, including study design and analytic approaches to avoid selection bias and other common pitfalls of quasi-experimental studies.
We assessed the frequency and relatedness of methicillin-resistant Staphylococcus aureus (MRSA) isolates to determine whether healthcare workers, the environment, or admitted patients could be a reservoir for MRSA on a burn trauma unit (BTU). We also assessed risk factors for MRSA colonization among BTU patients.
Prospective cohort study and surveillance for MRSA carriage.
BTU of a Midwestern academic medical center.
Patients and Participants.
Patients admitted to a BTU from February 2009 through January 2010 and healthcare workers on this unit during the same time period.
Samples for MRSA culture were collected on admission from the nares and wounds of all BTU patients. We also had collected culture samples from the throat, axilla, antecubital fossa, groin, and perianal area of 12 patients per month. Samples collected from healthcare workers' nares and from environmental sites were cultured quarterly. MRSA isolates were typed by pulsed-field gel electrophoresis.
Of 144 patients, 24 (17%) carried MRSA in their nares on admission. Male sex (odds ratio [OR], 5.51; 95% confidence interval [95% CI], 1.25–24.30), admission for necrotizing fasciitis (OR, 7.66; 95% CI, 1.64–35.81), and MRSA colonization of a site other than the nares (OR, 23.40; 95% CI, 6.93–79.01) were independent predictors of MRSA nasal carriage. Cultures of samples collected from 4 healthcare workers and 4 environmental cultures had positive results. Two patients were colonized with strains that were indistinguishable from strains collected from a healthcare worker or the environment.
Patients were a major reservoir for MRSA. Infection control efforts should focus on preventing transmission of MRSA from patients who are MRSA carriers to other patients on the unit.
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