Managing Stress and Pain to Prevent Patient Discomfort, Distress and Delayed Wound Healing.

Evidence suggests stress slows the healing of wounds but pain may also play a part. Regular assessment could improve patients' quality of life and recovery time.

Validation of Electronic Health Record Phenotyping of Bipolar Disorder Cases and Controls

Objective: The study was designed to validate use of elec-tronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. Method: EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype di- agnoses was calculated against diagnoses from direct semi- structured interviews of 190 patients by trained clinicians blind to EHR diagnosis. Results: The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR- classified control subject received a diagnosis of bipolar dis- order on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based clas- sifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. Conclusions: Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.

Improving the Psychometric Utility of the Hypomania Checklist (HCL-32): a Rasch Analysis Approach

Background The HCL-32 is a widely-used screening questionnaire for hypomania. We aimed to use a Rasch analysis approach to (i) evaluate the measurement properties, principally unidimensionality, of the HCL-32, and (ii) generate a score table to allow researchers to convert raw HCL-32 scores into an interval-level measurement which will be more appropriate for statistical analyses. Methods Subjects were part of the Bipolar Disorder Research Network (BDRN) study with DSM-IV bipolar disorder (n=389). Multidimensionality was assessed using the Rasch fit statistics and principle components analysis of the residuals (PCA). Item invariance (differential item functioning, DIF) was tested for gender, bipolar diagnosis and current mental state. Item estimates and reliabilities were calculated. Results Three items (29, 30, 32) had unacceptable fit to the Rasch unidimensional model. Item 14 displayed significant DIF for gender and items 8 and 17 for current mental state. Item estimates confirmed that not all items measure hypomania equally. Limitations This sample was recruited as part of a large ongoing genetic epidemiology study of bipolar disorder and may not be fully representative of the broader clinical population of individuals with bipolar disorder. Conclusion The HCL-32 is unidimensional in practice, but measurements may be further strengthened by the removal of four items. Re-scored linear measurements may be more appropriate for clinical research.