Emma Wallace

Managing patients with multimorbidity in primary care

#### The bottom line Multimorbidity, commonly defined as the presence of two or more chronic medical conditions in an individual,1 is associated with decreased quality of life, functional decline, and increased healthcare utilisation, including emergency admissions, particularly with higher numbers of coexisting conditions.2 3 4 5 6 The management of multimorbidity with drugs is often complex, resulting in polypharmacy with its attendant risks.7 8 9 Patients with multimorbidity have a high treatment burden in terms of understanding and self managing the conditions, attending multiple appointments, and managing complex drug regimens.10 Qualitative research highlights the “endless struggle” patients experience in trying to manage their conditions well.11 Psychological distress is common: in an Australian survey of 7620 patients in primary care, 23% of those with one chronic condition reported depression compared with 40% of those with five or more conditions.12 #### Sources and selection criteria We based this article on the authors’ experience and information from published literature. We carried out searches of PubMed and the Cochrane library using the search terms “co-morbidity” or “comorbidity” or “multimorbid” or “multimorbidity” or “multi-morbidity”. No MeSH term exists for multimorbidity. The searches were supplemented by a review of authors’ personal archives as well as relevant articles from the International Research …

Validation of the CHADS2 clinical prediction rule to predict ischaemic stroke. A systematic review and meta-analysis.

The CHADS2 predicts annual risk of ischaemic stroke in non-valvular atrial fibrillation. This systematic review and meta-analysis aims to determine the predictive value of CHADS2. The literature was systematically searched from 2001 to October 2010. Data was pooled and analysed using discrimination and calibration statistical measures, using a random effects model. Eight data sets (n = 2815) were included. The diagnostic accuracy suggested a cut-point of ≥ 1 has higher sensitivity (92%) than specificity (12%) and a cut-point of ≥ 4 has higher specificity (96%) than sensitivity (33%). Lower summary estimates were observed for cut-points ≥ 2 (sensitivity 79%, specificity 42%) and ≥ 3 (specificity 77%, sensitivity 50%). There was insufficient data to analyse cut-points ≥ 5 or ≥ 6. Moderate pooled c statistic values were identified for the classic (0.63, 95% CI 0.52-0.75) and revised (0.60, 95% CI 0.43-0.72) view of stratification of the CHADS2. Calibration analysis indicated no significant difference between the predicted and observed strokes across the three risk strata for the classic or revised view. All results were associated with high heterogeneity, and conclusions should be made cautiously. In conclusion, the pooled c statistic and calibration analysis suggests minimal clinical utility of both the classic and revised view of the CHADS2 in predicting ischaemic stroke across all risk strata. Due to high heterogeneity across studies and low event rates across all risk strata, the results should be interpreted cautiously. Further validation of CHADS2 should perhaps be undertaken, given the methodological differences between many of the available validation studies and the original CHADS2 derivation study.

The epidemiology of malpractice claims in primary care: a systematic review

Objectives The aim of this systematic review was to examine the epidemiology of malpractice claims in primary care. Design A computerised systematic literature search was conducted. Studies were included if they reported original data (≥10 cases) pertinent to malpractice claims, were based in primary care and were published in the English language. Data were synthesised using a narrative approach. Setting Primary care. Participants Malpractice claimants. Primary outcome Malpractice claim (defined as a written demand for compensation for medical injury). We recorded: medical misadventure cited in claims, missed/delayed diagnoses cited in claims, outcome of claims, prevalence of claims and compensation awarded to claimants. Results Of the 7152 articles retrieved by electronic search, a total of 34 studies met the inclusion criteria and were included in the narrative analysis. Twenty-eight studies presented data from medical indemnity malpractice claims databases and six studies presented survey data. Fifteen studies were based in the USA, nine in the UK, seven in Australia, one in Canada and two in France. The commonest medical misadventure resulting in claims was failure to or delay in diagnosis, which represented 26–63% of all claims across included studies. Common missed or delayed diagnoses included cancer and myocardial infarction in adults and meningitis in children. Medication error represented the second commonest domain representing 5.6–20% of all claims across included studies. The prevalence of malpractice claims in primary care varied across countries. In the USA and Australia when compared with other clinical disciplines, general practice ranked in the top five specialties accounting for the most claims, representing 7.6–20% of all claims. However, the majority of claims were successfully defended. Conclusions This review of malpractice claims in primary care highlights diagnosis and medication error as areas to be prioritised in developing educational strategies and risk management systems.

Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs)

Clinical Prediction Rules (CPRs) are tools that quantify the contribution of symptoms, clinical signs and available diagnostic tests, and in doing so stratify patients according to the probability of having a target outcome or need for a specified treatment. Most focus on the derivation stage with only a minority progressing to validation and very few undergoing impact analysis. Impact analysis studies remain the most efficient way of assessing whether incorporating CPRs into a decision making process improves patient care. However there is a lack of clear methodology for the design of high quality impact analysis studies.We have developed a sequential four-phased framework based on the literature and the collective experience of our international working group to help researchers identify and overcome the specific challenges in designing and conducting an impact analysis of a CPR.There is a need to shift emphasis from deriving new CPRs to validating and implementing existing CPRs. The proposed framework provides a structured approach to this topical and complex area of research.

Risk prediction models to predict emergency hospital admission in community-dwelling adults: a systematic review.

Background:Risk prediction models have been developed to identify those at increased risk for emergency admissions, which could facilitate targeted interventions in primary care to prevent these events. Objective:Systematic review of validated risk prediction models for predicting emergency hospital admissions in community-dwelling adults. Methods:A systematic literature review and narrative analysis was conducted. Inclusion criteria were as follows; Population: community-dwelling adults (aged 18 years and above); Risk: risk prediction models, not contingent on an index hospital admission, with a derivation and ≥1 validation cohort; Primary outcome: emergency hospital admission (defined as unplanned overnight stay in hospital); Study design: retrospective or prospective cohort studies. Results:Of 18,983 records reviewed, 27 unique risk prediction models met the inclusion criteria. Eleven were developed in the United States, 11 in the United Kingdom, 3 in Italy, 1 in Spain, and 1 in Canada. Nine models were derived using self-report data, and the remainder (n=18) used routine administrative or clinical record data. Total study sample sizes ranged from 96 to 4.7 million participants. Predictor variables most frequently included in models were: (1) named medical diagnoses (n=23); (2) age (n=23); (3) prior emergency admission (n=22); and (4) sex (n=18). Eleven models included nonmedical factors, such as functional status and social supports. Regarding predictive accuracy, models developed using administrative or clinical record data tended to perform better than those developed using self-report data (c statistics 0.63–0.83 vs. 0.61–0.74, respectively). Six models reported c statistics of >0.8, indicating good performance. All 6 included variables for prior health care utilization, multimorbidity or polypharmacy, and named medical diagnoses or prescribed medications. Three predicted admissions regarded as being ambulatory care sensitive. Conclusions:This study suggests that risk models developed using administrative or clinical record data tend to perform better. In applying a risk prediction model to a new population, careful consideration needs to be given to the purpose of its use and local factors.

Reducing emergency admissions through community based interventions

Evidence for current interventions is limited. Emma Wallace and colleagues discuss the alternatives

A Systematic Review of the Probability of Repeated Admission Score in Community-Dwelling Adults

To perform a systematic review of the Probability of Repeated Admission (Pra) score in community‐dwelling adults to assess its performance in a range of validation studies 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.

Clinical prediction rules in practice: review of clinical guidelines and survey of GPs

BACKGROUND The publication of clinical prediction rules (CPRs) studies has risen significantly. It is unclear if this reflects increasing usage of these tools in clinical practice or how this may vary across clinical areas. AIM To review clinical guidelines in selected areas and survey GPs in order to explore CPR usefulness in the opinion of experts and use at the point of care. DESIGN AND SETTING A review of clinical guidelines and survey of UK GPs. METHOD Clinical guidelines in eight clinical domains with published CPRs were reviewed for recommendations to use CPRs including primary prevention of cardiovascular disease, transient ischaemic attack (TIA) and stroke, diabetes mellitus, fracture risk assessment in osteoporosis, lower limb fractures, breast cancer, depression, and acute infections in childhood. An online survey of 401 UK GPs was also conducted. RESULTS Guideline review: Of 7637 records screened by title and/or abstract, 243 clinical guidelines met inclusion criteria. CPRs were most commonly recommended in guidelines regarding primary prevention of cardiovascular disease (67%) and depression (67%). There was little consensus across various clinical guidelines as to which CPR to use preferentially. SURVEY Of 401 responders to the GP survey, most were aware of and applied named CPRs in the clinical areas of cardiovascular disease and depression. The commonest reasons for using CPRs were to guide management and conform to local policy requirements. CONCLUSION GPs use CPRs to guide management but also to comply with local policy requirements. Future research could focus on which clinical areas clinicians would most benefit from CPRs and promoting the use of robust, externally validated CPRs.

Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish a web-based register

OBJECTIVES Identifying clinical prediction rules (CPRs) for primary care from electronic databases is difficult. This study aims to identify a search filter to optimize retrieval of these to establish a register of CPRs for the Cochrane Primary Health Care field. STUDY DESIGN AND SETTING Thirty primary care journals were manually searched for CPRs. This was compared with electronic search filters using alternative methodologies: (1) textword searching; (2) proximity searching; (3) inclusion terms using specific phrases and truncation; (4) exclusion terms; and (5) combinations of methodologies. RESULTS We manually searched 6,344 articles, revealing 41 CPRs. Across the 45 search filters, sensitivities ranged from 12% to 98%, whereas specificities ranged from 43% to 100%. There was generally a trade-off between the sensitivity and specificity of each filter (i.e., the number of CPRs and total number of articles retrieved). Combining textword searching with the inclusion terms (using specific phrases) resulted in the highest sensitivity (98%) but lower specificity (59%) than other methods. The associated precision (2%) and accuracy (60%) were also low. CONCLUSION The novel use of combining textword searching with inclusion terms was considered the most appropriate for updating a register of primary care CPRs where sensitivity has to be optimized.