Identifying Adverse Drug Events in Older Community-Dwelling Patients.

PURPOSE: To evaluate a patient-report instrument for identifying adverse drug events (ADEs) in older populations with multimorbidity in the community setting. METHODS: This was a retrospective cohort study of 859 community-dwelling patients aged ≥70 years treated at 15 primary care practices. Patients were asked if they had experienced any of a list of 74 symptoms classified by physiologic system in the previous 6 months and if (1) they believed the symptom to be related to their medication, (2) the symptom had bothered them, (3) they had discussed it with their family physician, and (4) they required hospital care due to the symptom. Self-reported symptoms were independently reviewed by 2 clinicians who determined the likelihood that the symptom was an ADE. Family physician medical records were also reviewed for any report of an ADE. RESULTS: The ADE instrument had an accuracy of 75% (95% CI, 77%-79%), a sensitivity of 29% (95% CI, 27%-31%), and a specificity of 93% (95% CI, 92%-94%). Older people who reported a symptom had an increased likelihood of an ADE (positive likelihood ratio [LR+]: 4.22; 95% CI, 3.78-4.72). Antithrombotic agents were the drugs most commonly associated with ADEs. Patients were most bothered by muscle pain or weakness (75%), dizziness or lightheadedness (61%), cough (53%), and unsteadiness while standing (52%). On average, patients reported 39% of ADEs to their physician. Twenty-six (3%) patients attended a hospital outpatient clinic, and 32 (4%) attended an emergency department due to ADEs. CONCLUSION: Older community-dwelling patients were often not correct in recognizing ADEs. The ADE instrument demonstrated good predictive value and could be used to differentiate between symptoms of ADEs and chronic disease in the community setting.

Developing an international register of clinical prediction rules for use in primary care: a descriptive analysis.

PURPOSE: We describe the methodology used to create a register of clinical prediction rules relevant to primary care. We also summarize the rules included in the register according to various characteristics. METHODS: To identify relevant articles, we searched the MEDLINE database (PubMed) for the years 1980 to 2009 and supplemented the results with searches of secondary sources (books on clinical prediction rules) and personal resources (eg, experts in the field). The rules described in relevant articles were classified according to their clinical domain, the stage of development, and the clinical setting in which they were studied. RESULTS: Our search identified clinical prediction rules reported between 1965 and 2009. The largest share of rules (37.2%) were retrieved from PubMed. The number of published rules increased substantially over the study decades. We included 745 articles in the register; many contained more than 1 clinical prediction rule study (eg, both a derivation study and a validation study), resulting in 989 individual studies. In all, 434 unique rules had gone through derivation; however, only 54.8% had been validated and merely 2.8% had undergone analysis of their impact on either the process or outcome of clinical care. The rules most commonly pertained to cardiovascular disease, respiratory, and musculoskeletal conditions. They had most often been studied in the primary care or emergency department settings. CONCLUSIONS: Many clinical prediction rules have been derived, but only about half have been validated and few have been assessed for clinical impact. This lack of thorough evaluation for many rules makes it difficult to retrieve and identify those that are ready for use at the point of patient care. We plan to develop an international web-based register of clinical prediction rules and computer-based clinical decision support systems.

Mental health diagnosis by nurses using the Global Mental Health Assessment Tool: a validity and feasibility study.

BACKGROUND: The Global Mental Health Assessment Tool-Primary Care Version (GMHAT/PC) has been developed to assist health professionals to make a quick and comprehensive standardised mental health assessment. It has proved to be a reliable and valid tool in a previous study involving GPs. Its use by other health professionals may help in detecting and managing mental disorders in primary care and general health settings. AIM: To assess the feasibility of using a computer-assisted diagnostic interview by nurses and to examine the level of agreement between the GMHAT/PC diagnosis and psychiatrists' clinical diagnosis. DESIGN OF STUDY: Cross-sectional validation study. SETTING: Primary care, general healthcare (cardiac rehabilitation clinic), and community mental healthcare settings. METHOD: A total of 215 patients between the ages of 16 and 75 years were assessed by nurses and psychiatrists in various settings: primary care centre (n = 54), cardiac rehabilitation centre (n = 98), and community mental health clinic (n = 63). The time taken for the interview, and feedback from patients and interviewers were indicators of feasibility, and the kappa coefficient (kappa), sensitivity, and specificity of the GMHAT/PC diagnosis were measures of validity. RESULTS: Mean duration of interview was under 15 minutes. The agreement between nurses' GMHAT/PC interview-based diagnosis and psychiatrists' International Classification of Diseases (ICD)-10 criteria-based clinical diagnosis was 80% (kappa = 0.76, sensitivity = 0.84, specificity = 0.92). CONCLUSION: The GMHAT/PC can assist nurses to make accurate mental health assessment and diagnosis in various healthcare settings and it is acceptable to patients.

The Global Mental Health Assessment Tool--Primary Care Version (GMHAT/PC). Development, reliability and validity.

The Global Mental Health Assessment Tool--Primary Care Version (GMHAT/PC) is a computerised clinical assessment tool developed to assess and identify a wide range of mental health problems in primary care. It generates a computer diagnosis, a symptom rating, a self-harm risk assessment, and a referral letter. Patients from primary care and community psychiatric outpatient clinics and a small sample of inpatients were interviewed for a period of two months using the GMHAT/PC. A proportion of patients were simultaneously rated by a psychiatrist and a general practitioner for inter-rater reliability. All patients also completed the Hospital Anxiety and Depression Scale (HAD). To conduct the interview was easy in all settings and took 10-15 minutes for patients who had psychiatric symptoms. Inter-rater agreement on mental state symptom groups ranged from 0.49 to 1 (kappa). The computer diagnosis correlated highly with the clinical diagnosis and there was a good level of agreement between HAD ratings and GMHAT/PC ratings. These data suggest that the GMHAT/PC is an easy to administer computerised tool which can be used in primary care for the standardised assessment of mental health problems.