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

Annette Plüddemann; Emma Wallace; Clare Bankhead; Claire Keogh; Danielle Van der Windt; Daniel Lasserson; Rose Galvin; Ivan Moschetti; Karen Kearley; Kirsty O’Brien; Sharon Sanders; Susan Mallett; Uriell Malanda; Matthew Thompson; Tom Fahey; Richard Stevens

doi:10.3399/bjgp14X677860

Abstract

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.

INTRODUCTION

Clinical guidelines are developed systematically based on best available evidence to aid clinical decision making.¹ The use of appropriately validated and tested clinical prediction rules (CPRs) is one way of implementing evidence-based medicine (EBM) for diagnosis and prognosis in clinical practice. CPRs are defined as tools that quantify the contributions of history, clinical examination, and diagnostic tests to stratify a patient in terms of the probability of having a target disorder (diagnostic CPR) or a future health outcome (prognostic CPR).¹ An example is the Goldman CPR, which uses a combination of clinical and electrocardiograph findings to risk-stratify patients presenting with chest pain as low, moderate, or high risk of a cardiac cause.² Smaller proportions of CPRs go further and recommend management decisions based on their algorithms, for instance, the modified Centor score for streptococcal throat infection stratifies patients based on symptoms and clinical signs and then uses this to direct the need for antibiotic prescription.³,⁴

However, there are well-recognised barriers to implementing CPRs at the point of care.⁵,⁶ One such barrier is a tendency to develop more CPRs for the same clinical situation, rather than validating existing models.⁷ The significant increase in the publication of CPRs in recent years suggests an increased interest on the part of researchers at least in such models.⁸,⁹ It is unclear if this reflects increasing usage of these tools in clinical practice or how this may vary across clinical areas.

This study investigated whether published CPRs have been considered useful by expert bodies and at the point of clinical care. To answer the first question, a review of international clinical guidelines produced on behalf of expert bodies was performed, and to answer the second, a well-defined group of UK clinicians, GPs were surveyed about their use of CPRs in selected clinical areas.

How this fits in

The use of appropriately validated and tested clinical prediction rules (CPRs) is one way of implementing evidence-based medicine for diagnosis and prognosis in clinical practice and publication of CPRs has risen significantly. This study showed that recommendation of CPRs by clinical guidelines varied according to clinical area. Surveyed GPs reported using CPRs most frequently in the clinical domains of cardiovascular disease and depression, primarily to guide management and adhere to local policy requirements. Future efforts could focus on determining in which areas of practice CPRs would be most beneficial for clinicians and patients, and promoting the use of robust, externally validated CPRs.

METHOD

Review of clinical guidelines

The aim was to identify clinical guidelines in eight selected areas in which the authors had prior knowledge that at least one CPR potentially relevant to primary care had been published:

primary prevention of cardiovascular disease (CVD);
TIA/stroke diagnosis and management;
diabetes mellitus screening, diagnosis or risk assessment;
fracture risk assessment in osteoporosis screening and management;
lower limb fractures diagnosis;
breast cancer diagnosis, screening, and risk assessment;
depression diagnosis and management; and
acute childhood infections, namely meningitis, influenza, urinary tract infection, gastroenteritis, otitis media, tonsillitis, pneumonia, and bronchiolitis.

Search strategy

A PubMed search used the ‘Practice Guideline’ publication type and was expanded to include documents with any of the words; ‘Guideline[s]’, ‘Framework’, ‘Standards’, ‘Recommendation[s]’, ‘Guidance’, ‘Consensus’, ‘Statement’ or ‘Practice Guideline’ in the title, producing a highly sensitive search (n = 106 088). To make the search more specific limits were applied: English language; exclusion of publication types News, Randomized Controlled Trial, Meta-Analysis, Clinical Trial, Letter and Comment; published between 1 June 2000 and 31 May 2010, resulting in 41 228 records. This guidelines search was then combined with subject-specific searches designed by researchers familiar with each of the clinical domains.

In addition, the websites of the National Guideline clearing house, the National Institute for Health and Clinical Excellence (NICE) and the Scottish Intercollegiate Guideline Network (SIGN) were accessed and searched (included as additional sources in Appendices 1a and 1b).

Study selection

Documents identified from these subject-specific searches were eligible for inclusion if they met the following criteria: (a) contains systematically developed statements that include recommendations, strategies, or information that assists clinicians and patients to make decisions about appropriate health care for specific clinical presentations; (b) produced by medical speciality associations — relevant professional societies, public or private organisations, government agencies, or healthcare providers at the state, national, or international level; (c) full text freely available in print or electronic format; and (d) current and most recent available version of the guideline available. Documents identified by the search and meeting the above four criteria were considered to be ‘clinical guidelines’ for the purposes of this review.

The difficulty of formally defining CPRs has been discussed previously.⁸ For the purposes of this review, a pre-existing definition¹⁰ was adapted to define a clinical prediction rule as ‘a predefined combination of (two or more) questions, symptoms, signs and tests that provides information on risk, diagnosis, or prognosis’. Formal diagnostic criteria were not considered to be CPRs. For the purposes of this review, a CPR was considered to be ‘predefined’ if the guideline cites an article on the CPR in a peer-reviewed journal.

Data extraction

In each clinical area, one researcher searched through titles and abstracts for their specific search. Each potentially relevant full-text article was independently reviewed in duplicate and relevant data extracted. To be considered to be ‘recommended’ by the guideline, use of language that recommends, encourages, or promotes the use of the CPR was required. Discrepancies were resolved by consensus or by a third adjudicating reviewer. For each clinical area the total number of guidelines retrieved, the number and proportion recommending use of at least one CPR, and the most commonly recommended CPRs are reported. For the acute infections in children domain, guidelines were included if children were mentioned specifically in the title or if the guideline could be applied to both adults and children.

Survey of GPs

Participants

Participants were GPs in the UK, registered with the General Medical Council, recruited from Doctors.net.uk. To estimate the percentage using each CPR with a standard error of approximately 2.5%, a sample size of 400 GPs stratified by NHS Strategic Health Authority and seniority/position was requested. Doctors.net.uk sent invitations to members followed by reminders until 400 GPs had completed the questionnaire. Participants were asked for their year of qualification, and role in the practice.

Survey

In consultation with academic GP colleagues, 25 CPRs potentially relevant to UK general practice were selected. Modifications made after the in-house pilot included the addition of four CPRs: one of these, the NICE traffic light algorithm for childhood infection, did not meet the criteria for a CPR in the review of guidelines, but was considered a CPR by participants in the pilot survey. The resulting 29 included CPRs were grouped under six clinical areas, for presentation to survey responders, as shown in Table 1. Pragmatic considerations regarding survey length precluded the inclusion of all clinical areas studied in the first part of this study. Responders were asked which CPRs they had heard of and how often they used them. They were asked for reasons why they did or did not use each CPR, using the following options: (a) aid diagnosis; (b) assessing severity; (c) to guide therapy; (d) to guide referral; (e) comply with clinical guidelines/Quality Outcomes Framework (QOF); (f) automatically generated by practice software; and (g) inform or educate patients. A free text field was provided for other reasons for not using CPRs or to indicate any additional CPRs not included in the survey.

View this table:

Table 1.

List of clinical prediction rules (CPRs) included in the survey

RESULTS

Review of clinical guidelines

An overview of the search strategy is presented in Appendix 1a and 1b. A total of 7637 records were screened by title and/or abstract and 243 eligible clinical guidelines in eight clinical areas were identified and included in the review.

A summary of clinical guideline numbers retrieved and named CPRs recommended according to clinical domain is presented in Table 2. Overall, CPRs were most commonly recommended in the clinical domains of primary prevention of cardiovascular disease (67%), depression (67%), TIA/stroke (63%), and breast cancer (51%). For lower limb fractures and fracture risk assessment in osteoporosis, CPRs were recommended in 40% and 38% of reviewed guidelines respectively. CPRs were least often recommended in the clinical domains of diabetes mellitus (20%) and acute childhood infections (16%). Overall, there was little consensus across reviewed guidelines as to which, if any, CPR to use preferentially.

View this table:

Table 2.

Clinical guidelines review of clinical prediction rules (CPRs) recommendations

Survey of UK GPs

A total of 401 responses were collected from Scotland (12%), Northern Ireland (3%), Wales (4.5%), and England (80.5%), spread among each of the 10 English Strategic Health Authority regions. Participants qualified between 1969 and 2005 (median 1995) and most (65%) were GP principals or partners. Figure 1 shows the reported frequency of use of any CPRs in each clinical domain, of specific CPRs indicated by name, and of other CPRs not included in the questionnaire design but named by responders in the free text field. In CVD these other CPRs included ASSIGN (n = 23 GPs, all based in Scotland) and the UKPDS Risk Engine (n = 1). In depression, other CPRs included the Edinburgh Postnatal Depression Scale (24 GPs) and Mini-Mental State Examination for dementia (n = 3).¹¹,¹² In addition to the fracture CPRs listed in the questionnaire, seven used the FRAX tool for osteoporosis.¹³ In cancer, additional CPRs mentioned were the Gleason score for prostate cancer staging (n = 13 GPs),¹⁴ Dukes staging for colorectal cancer (n = 5),¹⁵ and the Tumour Nodes Metastases cancer staging system (n = 4).¹⁶ In addition to infection CPRs listed in the survey, five (1%) reported using the CURB65 score¹⁷ and two (0.5%) reported having used the APACHE II score for ICU mortality.¹⁸ In the section entitled general medical, additional CPRs mentioned were the Glasgow Coma Scale (n = 11 GPs), Epworth sleepiness scale (n = 5), and Rockall score for risk of upper gastrointestinal bleeding (n = 3).¹⁹ Responders additionally used the free text to report using the International Prostate Symptom Score for benign prostatic hyperplasia (n = 7),²⁰ various alcohol use questionnaires (n = 6), and others (all n≤2). Reported reasons for CPR use are presented in Table 3.

Figure 1.

Self-reported use of clinical prediction rules by 401 UK GPs in selected clinical areas.

View this table:

Table 3.

Number (%) of GPs reporting respective reasons for using clinical prediction rules

The main reason for not using named CPRs related to lack of familiarity (Table 4). Other reasons, reported in free text fields, included preference for own clinical judgement (for CPRs listed under depression, infection, and general medical), greater relevance to secondary care settings (fracture and cancer), and perceived lack of utility (depression and cancer).

View this table:

Table 4.

Number (%) of GPs who do not use the respective clinical prediction rules and of these, the number (%) who had never heard of them

DISCUSSION

Summary

Of the eight clinical domains studied, guidelines most commonly recommended CPRs for primary prevention of CVD and depression. For CVD, a total of 10 different cardiovascular risk assessment models were recommended, most commonly those derived from the Framingham Heart Study. Surveyed GPs reported using these tools in practice also, with most using Framingham derived scores, the Joint British Societies risk score, or QRISK, primarily to guide therapy. Other reported reasons for use of these CPRs were to inform or educate patients, comply with guidelines/Quality and Outcomes Framework (QOF), and to assess disease severity. For depression, a total of 13 different models were recommended in eight reviewed guidelines, most commonly the PHQ-9. This was also utilised by most of the surveyed GPs who indicated that guideline or QOF conformance was the most common reason for use, followed by assessing severity and as a diagnostic aid. The Ottawa ankle rule for ankle fracture assessment, although infrequently recommended by reviewed guidelines, was used by most of the surveyed GPs, primarily to aid diagnosis. For breast cancer, although about half of reviewed guidelines recommended the use of a CPR model for risk assessment, these tools were very infrequently utilised by surveyed GPs. Most were either unaware of these tools or preferred to use UK referral guidelines, which dictate that suspected cancer cases need specialist review within 2 weeks.

Both the survey and review suggest that there are varying influences regarding use of CPRs in clinical practice. Use of these tools may vary geographically as illustrated by the guidelines review, where the QRISK2 score was recommended by UK guidelines only, and within the UK by the survey, with the ASSIGN algorithm being used exclusively by Scottish GPs. As already mentioned, national policy requirements in UK general practice also have an impact on CPR uptake. Overall, a lack of familiarity, preference for their own clinical judgement, or considering the CPR to be unnecessary were highlighted by surveyed GPs as the main impediments for use of these tools at the point of care. Examining the level of evidence for all CPRs included in this review was beyond the scope of this study. However, it is interesting to note that the Centor score for streptococcal throat infection, which has been broadly validated for use in general practice, was not used by most GPs (81%), with 76% of these reporting they had never heard of it, whereas the NICE traffic light system for the assessing childhood fever, which was developed for the purposes of the guideline, was used by almost half of the surveyed GPs. CPR use also varied according to clinical area, for example the CHADS and CHADS2 score for the prediction stroke risk in patients with atrial fibrillation and the Well’s score for DVT were not utilised by most surveyed GPs, although most were familiar with these CPRs.

Clinical guidelines offer scope to critically appraise published CPRs, which could help clinicians in making an informed decision regarding their use. However, in this review there was little such evaluation of CPRs evident and little consensus between guidelines as to which, if any, of these tools should be used preferentially.

Strengths and limitations

This study reviews wide-ranging international literature supplemented by a detailed survey of actual clinical practice in UK general practice. Each review was designed by a researcher with experience in the relevant area. The pre-selection of clinical domains in which CPRs exist allows comparison in terms of guideline recommendations and use in practice.

There are several limitations. First, for the purpose of rigorous review it was necessary to adopt a single, objective definition of a CPR. As there is no internationally agreed definition of a CPR, the definition used should be considered a working definition for a specific project rather than definitive for all purposes. Second, the literature review, of international scale and across eight clinical areas, is supplemented by a survey of primary care in a single national setting. The authors did not have the resources to conduct an international survey of primary, secondary, and tertiary care, and this led to a UK survey that looked at fewer CPRs than the literature review. Finally, the electronic survey mechanism used for this study gives no known denominator and represents a partially self-selecting population.

Comparison with existing literature

Previous studies of clinical prediction rules across multiple domains have assessed the properties of the rules, such as validity and impact, rather than their uptake by practicing clinicians⁷,⁹ Surveys of the uptake of CPRs have usually been restricted to single clinical domains.⁹¹–⁹⁴ To the author’s knowledge this is the first large survey to compare the uptake of CPRs across multiple clinical domains and to relate this to a systematic evaluation of guideline recommendations.

Implications for research and practice

From a clinical perspective, CPRs were applied by surveyed GPs most frequently in the clinical domains of CVD and depression mainly to guide management and adhere to local policy requirements. Lack of awareness was cited as one of the reasons for not using CPRs in practice. Future efforts could focus on determining in which areas of practice CPRs would be most beneficial to clinicians and patients, for example, referral guidance at the primary–secondary care interface for high stakes diagnoses such as myocardial infarction and cancer. In addition, the implementation of poorly validated CPRs should be resisted. Research should instead be directed to developing robust, externally validated CPRs that have been shown to have a positive impact on the process and outcome of clinical care. It is these CPRs that should be promoted in clinical guidelines.

Acknowledgments

The authors are grateful to Dr Grainne Cousins and Dr Daniel Brandt for assistance with the literature searches.

Appendix

Appendices 1a and 1b.

Literature search for clinical practice guidelines.

Notes

Funding

The authors wrote this paper for the International Diagnosis and Prognosis Prediction (IDAPP) working group. The survey of GPs represents independent research funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research programme (RP-PG-0407–10347). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. The guidelines review is supported by the Health Research Board (HRB) of Ireland through the HRB Centre for Primary Care Research under Grant HRC/2007/1.

Ethical approval

Not applicable.

Provenance

Freely submitted; externally peer reviewed.

Competing interests

Richard Stevens is an author of the UKPDS Risk Engine, one of the clinical prediction rules studied in this manuscript. He has no financial stake in the Risk Engine.

Discuss this article

Contribute and read comments about this article: www.bjgp.org/letters

Footnotes

↵* co-first authorship.

Received September 30, 2013.
Revision received November 7, 2013.
Accepted December 27, 2013.

REFERENCES

1.↵
1. McGinn TG,
2. Guyatt GH,
3. Wyer PC,
4. Naylor CD,
5. Stiell IG,
6. Richardson WS
(2000) Users’ guides to the medical literature: XXII: how to use articles about clinical decision rules. Evidence-Based Medicine Working Group. JAMA 284(1):79–84.
OpenUrl CrossRef PubMed
2.↵
1. Goldman L,
2. Cook EF,
3. Brand DA,
4. et al.
(1988) A computer protocol to predict myocardial infarction in emergency department patients with chest pain. N Engl J Med 318(13):797–803.
OpenUrl PubMed
3.↵
1. Centor RM,
2. Witherspoon JM,
3. Dalton HP,
4. et al.
(1981) The diagnosis of strep throat in adults in the emergency room. Med Decis Making 1(3):239–246.
OpenUrl FREE Full Text
4.↵
1. Singh S,
2. Dolan JG,
3. Centor RM
(2006) Optimal management of adults with pharyngitis – a multi-criteria decision analysis. BMC Med Inform Decis Mak 6:14.
OpenUrl PubMed
5.↵
1. Lang ES,
2. Wyer PC,
3. Haynes RB
(2007) Knowledge translation: closing the evidence-to-practice gap. Ann Emerg Med 49(3):355–363.
OpenUrl CrossRef PubMed
6.↵
1. Reilly BM,
2. Evans AT
(2006) Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med 144(3):201–209.
OpenUrl CrossRef PubMed
7.↵
1. Keogh C,
2. Fahey T
(2010) Clinical Prediction Rules in Primary Care: what can be done to maximise their implementation? Clinical Evidence, http://clinicalevidence.bmj.com/x/mce/file/05-10-10.pdf (accessed 26 Feb 2014).
8.↵
1. Keogh C,
2. Wallace E,
3. O’Brien KK,
4. et al.
Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish a Web-based register. J Clin Epidemiol 64(8):848–860.
9.↵
1. Toll DB,
2. Janssen KJ,
3. Vergouwe Y,
4. Moons KG
(2008) Validation, updating and impact of clinical prediction rules: a review. J Clin Epidemiol 61(11):1085–1094.
OpenUrl CrossRef PubMed
10.↵
1. Laupacis A,
2. Sekar N,
3. Stiell IG
(1997) Clinical prediction rules. A review and suggested modifications of methodological standards. JAMA 277(6):488–494.
OpenUrl CrossRef PubMed
11.↵
1. Cox JL,
2. Holden JM,
3. Sagovsky R
(1987) Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. Br J Psychiatry 150:782–786.
OpenUrl Abstract/FREE Full Text
12.↵
1. Folstein MF,
2. Folstein SE,
3. McHugh PR
(1975) ‘Mini-mental state’. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12(3):189–198.
OpenUrl CrossRef PubMed
13.↵
1. Kanis JA,
2. Johnell O,
3. Oden A,
4. et al.
(2008) FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int 19(4):385–397.
OpenUrl CrossRef PubMed
14.↵
1. Tannenbaum M
1. Gleason DF
(1977) in Urologic pathology: the prostate, The Veteran’s Administration Cooperative Urologic Research Group: histologic grading and clinical staging of prostatic carcinoma, ed Tannenbaum M (Lea and Febiger, Phildelphia, PA), pp 171–198.
15.↵
1. Dukes C
(1932) The classification of cancer of the rectum. J Pathol Bacteriol 35:323–332.
OpenUrl CrossRef
16.↵
1. Denoix PF
(1946) Enquete permanent dans les centres anticancereaux. [Continuous survey in cancer treatment centres] Bull Inst Nat Hyg 1:70–75.
OpenUrl
17.↵
1. Lim WS,
2. van der Eerden MM,
3. Laing R,
4. et al.
(2003) Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax 58(5):377–382.
OpenUrl Abstract/FREE Full Text
18.↵
1. Knaus WA,
2. Draper EA,
3. Wagner DP,
4. Zimmerman JE
(1985) APACHE II: a severity of disease classification system. Crit Care Med 13(10):818–829.
OpenUrl CrossRef PubMed
19.↵
1. Teasdale G,
2. Jennett B
(1974) Assessment of coma and impaired consciousness. A practical scale. Lancet 2(7872):81–84.
OpenUrl CrossRef PubMed
20.↵
1. Barry MJ,
2. Fowler FJ Jr.,
3. O’Leary MP,
4. et al.
(1992) The American Urological Association symptom index for benign prostatic hyperplasia. The Measurement Committee of the American Urological Association. J Urol 148(5):1549–1557.
OpenUrl PubMed
21.
1. Hippisley-Cox J,
2. Coupland C,
3. Vinogradova Y,
4. et al.
(2007) Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ 335(7611):136.
OpenUrl Abstract/FREE Full Text
22.
(2005) JBS 2: Joint British Societies’ guidelines on prevention of cardiovascular disease in clinical practice. Heart 91(Suppl 5):v1–52.
OpenUrl FREE Full Text
23.
1. Jackson R
(2000) Updated New Zealand cardiovascular disease risk-benefit prediction guide. BMJ 320(7236):709–710.
OpenUrl FREE Full Text
24.
1. Haq IU,
2. Jackson PR,
3. Yeo WW,
4. Ramsay LE
(1995) Sheffield risk and treatment table for cholesterol lowering for primary prevention of coronary heart disease. Lancet 346(8988):1467–1471.
OpenUrl CrossRef PubMed
25.
1. Anderson KM,
2. Wilson PW,
3. Odell PM,
4. Kannel WB
(1991) An updated coronary risk profile. A statement for health professionals. Circulation 83(1):356–362.
OpenUrl FREE Full Text
26.
1. Conroy RM,
2. Pyorala K,
3. Fitzgerald AP,
4. et al.
(2003) Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 24(11):987–1003.
OpenUrl Abstract/FREE Full Text
27.
1. Assmann G,
2. Cullen P,
3. Schulte H
(2002) Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Munster (PROCAM) study. Circulation 105(3):310–315.
OpenUrl Abstract/FREE Full Text
28.
1. Kroenke K,
2. Spitzer RL,
3. Williams JB
(2003) The Patient Health Questionnaire-2: validity of a two-item depression screener. Med Care 41(11):1284–1292.
OpenUrl CrossRef PubMed
29.
1. Kroenke K,
2. Spitzer RL,
3. Williams JB
(2001) The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 16(9):606–613.
OpenUrl CrossRef PubMed
30.
1. Spitzer RL,
2. Kroenke K,
3. Williams JB,
4. Lowe B
(2006) A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med 166(10):1092–1097.
OpenUrl CrossRef PubMed
31.
1. Zigmond AS,
2. Snaith RP
(1983) The hospital anxiety and depression scale. Acta Psychiatr Scand 67(6):361–370.
OpenUrl CrossRef PubMed
32.
1. Beck AT,
2. Steer RA,
3. Ball R,
4. Ranieri W
(1996) Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. J Pers Assess 67(3):588–597.
OpenUrl CrossRef PubMed
33.
1. Stiell IG,
2. Greenberg GH,
3. McKnight RD,
4. et al.
(1993) Decision rules for the use of radiography in acute ankle injuries. Refinement and prospective validation. JAMA 269(9):1127–1132.
OpenUrl CrossRef PubMed
34.
1. Stiell IG,
2. Greenberg GH,
3. Wells GA,
4. et al.
(1995) Derivation of a decision rule for the use of radiography in acute knee injuries. Ann Emerg Med 26(4):405–413.
OpenUrl CrossRef PubMed
35.
1. Seaberg DC,
2. Jackson R
(1994) Clinical decision rule for knee radiographs. Am J Emerg Med 12(5):541–543.
OpenUrl CrossRef PubMed
36.
1. Gail MH,
2. Brinton LA,
3. Byar DP,
4. et al.
(1989) Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 81(24):1879–1886.
OpenUrl Abstract/FREE Full Text
37.
1. Coulson AS,
2. Glasspool DW,
3. Fox J,
4. Emery J
(2001) RAGs: a novel approach to computerized genetic risk assessment and decision support from pedigrees. Methods Inf Med 40(4):315–322.
OpenUrl PubMed
38.
1. McCarthy PL,
2. Sharpe MR,
3. Spiesel SZ,
4. et al.
(1982) Observation scales to identify serious illness in febrile children. Pediatrics 70(5):802–809.
OpenUrl Abstract/FREE Full Text
39.
1. NICE. Feverish illness in children (CG47) (replaced by CG160)
Feverish illness in children — Assessment and initial management in children younger than 5 years, http://guidance.nice.org.uk/CG47 (accessed 26 Feb 2014).
40.
1. McIsaac WJ,
2. Kellner JD,
3. Aufricht P,
4. et al.
(2004) Empirical validation of guidelines for the management of pharyngitis in children and adults. JAMA 291(13):1587–1595.
OpenUrl CrossRef PubMed
41.
1. Rothwell PM,
2. Giles MF,
3. Flossmann E,
4. et al.
(2005) A simple score (ABCD) to identify individuals at high early risk of stroke after transient ischaemic attack. Lancet 366(9479):29–36.
OpenUrl CrossRef PubMed
42.
1. Johnston SC,
2. Gress DR,
3. Browner WS,
4. Sidney S
(2001) Short-term prognosis after emergency department diagnosis of TIA. JAMA 284(22):2901–2906.
OpenUrl
43.
1. Gage BF,
2. Waterman AD,
3. Shannon W,
4. et al.
(2001) Validation of clinical classification schemes for predicting stroke: results from the National Registry of Atrial Fibrillation. JAMA 285(22):2864–2870.
OpenUrl CrossRef PubMed
44.
1. Wells PS,
2. Hirsh J,
3. Anderson DR,
4. et al.
(1998) A simple clinical model for the diagnosis of deep-vein thrombosis combined with impedance plethysmography: potential for an improvement in the diagnostic process. J Intern Med 243(1):15–23.
OpenUrl CrossRef PubMed
45.
1. Wells PS,
2. Anderson DR,
3. Rodger M,
4. et al.
(2000) Derivation of a simple clinical model to categorize patients probability of pulmonary embolism: increasing the models utility with the SimpliRED D-dimer. Thromb Haemost 83(3):416–420.
OpenUrl PubMed
46.
1. Kothari V,
2. Stevens RJ,
3. Adler AI,
4. et al.
(2002) UKPDS 60: risk of stroke in type 2 diabetes estimated by the UK Prospective Diabetes Study risk engine. Stroke 33(7):1776–1781.
OpenUrl Abstract/FREE Full Text
47.
(2003) 2003 European Society of Hypertension-European Society of Cardiology guidelines for the management of arterial hypertension. J Hypertens 21(6):1011–1053.
OpenUrl CrossRef PubMed
48.
1. Grover SA,
2. Abrahamowicz M,
3. Joseph L,
4. et al.
(1992) The benefits of treating hyperlipidemia to prevent coronary heart disease. Estimating changes in life expectancy and morbidity. JAMA 267(6):816–822.
OpenUrl CrossRef PubMed
49.
1. Ridker PM,
2. Buring JE,
3. Rifai N,
4. Cook NR
(2007) Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA 297(6):611–619.
OpenUrl CrossRef PubMed
50.
1. Woodward M,
2. Brindle P,
3. TunstallĐPedoe H
(2007) Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC) Heart 93(2):172–176.
OpenUrl Abstract/FREE Full Text
51.
1. Chalmers J,
2. MacMahon S,
3. Mancia G,
4. et al.
(1999) 1999 World Health Organization - International Society of Hypertension Guidelines for the management of hypertension. Guidelines sub-committee of the WHO. Clin Exp Hypertens 21(5–6):1009–1060.
OpenUrl CrossRef PubMed
52.
1. Johnston SC,
2. Rothwell PM,
3. Nguyen-Huynh MN,
4. et al.
(2007) Validation and refinement of scores to predict very early stroke risk after transient ischaemic attack. Lancet 369(9558):283–292.
OpenUrl CrossRef PubMed
53.
1. Schnabel RB,
2. Sullivan LM,
3. Levy D,
4. et al.
(2009) Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet 373(9665):739–745.
OpenUrl CrossRef PubMed
54.
1. Harbison J,
2. Hossain O,
3. Jenkinson D,
4. et al.
(2003) Diagnostic accuracy of stroke referrals from primary care, emergency room physicians, and ambulance staff using the face arm speech test. Stroke 34(1):71–76.
OpenUrl Abstract/FREE Full Text
55.
1. Nor AM,
2. Davis J,
3. Sen B,
4. et al.
The Recognition of Stroke in the Emergency Room (ROSIER) scale: development and validation of a stroke recognition instrument. Lancet Neurol 4(11):727–734.
56.
1. Bray JE,
2. Martin J,
3. Cooper G,
4. et al.
(2005) Paramedic identification of stroke: community validation of the Melbourne ambulance stroke screen. Cerebrovasc Dis 20(1):28–33.
OpenUrl CrossRef PubMed
57.
1. Goldberg D,
2. Williams P
(1988) A user’s guide to the General Health Questionnaire (NFER-Nelson, Basingstoke).
58.
1. Sutcliffe LM,
2. Lincoln NB
(1998) The assessment of depression in aphasic stroke patients: the development of the Stroke Aphasic Depression Questionnaire. Clin Rehabil 12(6):506–513.
OpenUrl Abstract/FREE Full Text
59.
1. Malnutrition Advisory Group
(2000) MAG guidelines for detection and management of malnutrition (British Association for Parenteral and Enteral Nutrition (BAPEN) Maidenhead).
60.
1. Heikes KE,
2. Eddy DM,
3. Arondekar B,
4. Schlessinger L
(2008) Diabetes Risk Calculator: a simple tool for detecting undiagnosed diabetes and pre-diabetes. Diabetes Care 31(5):1040–1045.
OpenUrl Abstract/FREE Full Text
61.
1. Lindstrom J,
2. Tuomilehto J
(2003) The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 26(3):725–731.
OpenUrl Abstract/FREE Full Text
62.
1. Cadarette SM,
2. Jaglal SB,
3. Kreiger N,
4. et al.
(2000) Development and validation of the Osteoporosis Risk Assessment Instrument to facilitate selection of women for bone densitometry. CMAJ 162(9):1289–1294.
OpenUrl Abstract/FREE Full Text
63.
1. Lydick E,
2. Cook K,
3. Turpin J,
4. et al.
(1998) Development and validation of a simple questionnaire to facilitate identification of women likely to have low bone density. Am J Manag Care 4(1):37–48.
OpenUrl PubMed
64.
1. Lowery DJ,
2. Farley TD,
3. Wing DW,
4. et al.
(2006) A clinical composite score accurately detects meniscal pathology. Arthroscopy 22(11):1174–1179.
OpenUrl PubMed
65.
1. de Bie RA,
2. de Vet HC,
3. van den Wildenberg FA,
4. et al.
(1997) The prognosis of ankle sprains. Int J Sports Med 18(4):285–289.
OpenUrl PubMed
66.
1. Fine MJ,
2. Auble TE,
3. Yealy DM,
4. et al.
(1997) A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med 336(4):243–250.
OpenUrl CrossRef PubMed
67.
(1991) WHO guidelines on detecting pneumonia in children. Lancet 338(8780):1453–1454.
OpenUrl PubMed
68.
1. Dubos F,
2. Moulin F,
3. Raymond J,
4. et al.
(2007) [Distinction between bacterial and aseptic meningitis in children: refinement of a clinical decision rule] Arch Pediatr 14(5):434–438.
OpenUrl CrossRef PubMed
69.
1. Nigrovic LE,
2. Kuppermann N,
3. Malley R
(2002) Development and validation of a multivariable predictive model to distinguish bacterial from aseptic meningitis in children in the post-Haemophilus influenzae era. Pediatrics 110(4):712–719.
OpenUrl Abstract/FREE Full Text
70.
1. Hoen B,
2. Viel JF,
3. Paquot C,
4. et al.
(1995) Multivariate approach to differential diagnosis of acute meningitis. Eur J Clin Microbiol Infect Dis 14(4):267–274.
OpenUrl CrossRef PubMed
71.
1. Sinclair JF,
2. Skeoch CH,
3. Hallworth D
(1987) Prognosis of meningococcal septicaemia. Lancet 2(8549):38.
OpenUrl PubMed
72.
1. Chen J,
2. Pee D,
3. Ayyagari R,
4. et al.
(2006) Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. J Natl Cancer Inst 98(17):1215–1226.
OpenUrl Abstract/FREE Full Text
73.
1. Claus EB,
2. Risch N,
3. Thompson WD
(1991) Genetic analysis of breast cancer in the cancer and steroid hormone study. Am J Hum Genet 48(2):232–242.
OpenUrl PubMed
74.
1. Parmigiani G,
2. Berry D,
3. Aguilar O
(1998) Determining carrier probabilities for breast cancer-susceptibility genes BRCA1 and BRCA2. Am J Hum Genet 62(1):145–158.
OpenUrl CrossRef PubMed
75.
1. Glas AM,
2. Floore A,
3. Delahaye LJ,
4. et al.
(2006) Converting a breast cancer microarray signature into a high-throughput diagnostic test. BMC Genomics 7:278.
OpenUrl CrossRef PubMed
76.
1. Paik S,
2. Shak S,
3. Tang G,
4. et al.
(2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351(27):2817–2826.
OpenUrl CrossRef PubMed
77.
1. Ravdin PM,
2. Siminoff LA,
3. Davis GJ,
4. et al.
(2001) Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol 19(4):980–991.
OpenUrl Abstract/FREE Full Text
78.
1. Blamey RW
(1996) The design and clinical use of the Nottingham Prognostic Index in breast cancer. Breast 5:156–157.
OpenUrl CrossRef
79.
1. Evans DG,
2. Eccles DM,
3. Rahman N,
4. et al.
(2004) A new scoring system for the chances of identifying a BRCA1/2 mutation outperforms existing models including BRCAPRO. J Med Genet 41(6):474–480.
OpenUrl Abstract/FREE Full Text
80.
1. Gilpin CA,
2. Carson N,
3. Hunter AG
(2000) A preliminary validation of a family history assessment form to select women at risk for breast or ovarian cancer for referral to a genetics center. Clin Genet 58(4):299–308.
OpenUrl CrossRef PubMed
81.
1. Tyrer J,
2. Duffy SW,
3. Cuzick J
(2004) A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 23(7):1111–1130.
OpenUrl CrossRef PubMed
82.
1. Hamilton M
(1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23:56–62.
OpenUrl FREE Full Text
83.
1. Montgomery SA,
2. Asberg M
(1979) A new depression scale designed to be sensitive to change. Br J Psychiatry 134:382–389.
OpenUrl Abstract/FREE Full Text
84.
1. Rush AJ,
2. Trivedi MH,
3. Ibrahim HM,
4. et al.
(2003) The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry 54(5):573–583.
OpenUrl CrossRef PubMed
85.
1. Alexopoulos GS,
2. Abrams RC,
3. Young RC,
4. Shamoian CA
(1988) Cornell Scale for Depression in Dementia. Biol Psychiatry 23(3):271–284.
OpenUrl CrossRef PubMed
86.
1. Bech P,
2. Rafaelsen OJ
(1980) The use of rating scales exemplified by a comparison of the Hamilton and the Bech-Rafaelsen Melancholia Scale. Acta Psychiatr Scand 62(Suppl 285):128–132.
OpenUrl CrossRef
87.
1. Guy W
(1976) ECDEU Assessment Manual for Psychopharmacology — Revised (Department of Health, Education, and Welfare, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, NIMH Psychopharmacology Research Branch, Division of Extramural Research Programs, Rockville, MD) (DHEW Publ No ADM 76–338).
88.
1. Yesavage JA,
2. Brink TL,
3. Rose TL,
4. et al.
(1982) Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res 17(1):37–49.
OpenUrl CrossRef PubMed
89.
1. Radloff LS
(1977) The CES-D scale: A self-report depression scale for research in the general population. Appl Psycholog Measure 1:385–401.
OpenUrl Abstract
90.
1. Fleming M,
2. Barry K
1. Brown RL
(1992) in Addictive disorders, Identification and office management of alcohol and drug disorders, eds Fleming M, Barry K (Mosby, St. Louis, MO), pp 25–43.
91.↵
1. Beswick AD,
2. Brindle P,
3. Fahey T,
4. Ebrahim S
(May, 2008) NICE Clinical Guidelines, No. 67S, A Systematic Review of Risk Scoring Methods and Clinical Decision Aids Used in the Primary Prevention of Coronary Heart Disease (Supplement) (Royal College of General Practitioners (UK) London).
92.
1. Dallongeville J,
2. Banegas JR,
3. Tubach F,
4. et al.
(2012) Survey of physicians’ practices in the control of cardiovascular risk factors: the EURIKA study. Eur J Prev Cardiol 19(3):541–550.
OpenUrl Abstract/FREE Full Text
93.
1. Porche K,
2. Reymond L,
3. Callaghan JO,
4. Charles M
(2014) Depression in palliative care patients: a survey of assessment and treatment practices of Australian and New Zealand palliative care specialists. Aust Health Rev 38(1):44–50.
OpenUrl
94.↵
1. Edwards QT,
2. Maradiegue A,
3. Seibert D,
4. et al.
(2009) Breast cancer risk elements and nurse practitioners’ knowledge, use, and perceived comfort level of breast cancer risk assessment. J Am Acad Nurse Pract 21(5):270–277.
OpenUrl PubMed

In this issue

Download PDF

Download PowerPoint

Email Article

Citation Tools

Keywords

More in this TOC Section

Show more Research

Cited By...

Intended for Healthcare Professionals

[1] 1.↵
McGinn TG,
Guyatt GH,
Wyer PC,
Naylor CD,
Stiell IG,
Richardson WS
(2000) Users’ guides to the medical literature: XXII: how to use articles about clinical decision rules. Evidence-Based Medicine Working Group. JAMA 284(1):79–84.
OpenUrl CrossRef PubMed

[2] McGinn TG,

[3] Guyatt GH,

[4] Wyer PC,

[5] Naylor CD,

[6] Stiell IG,

[7] Richardson WS

[8] 2.↵
Goldman L,
Cook EF,
Brand DA,
et al.
(1988) A computer protocol to predict myocardial infarction in emergency department patients with chest pain. N Engl J Med 318(13):797–803.
OpenUrl PubMed

[9] Goldman L,

[10] Cook EF,

[11] Brand DA,

[12] et al.

[13] 3.↵
Centor RM,
Witherspoon JM,
Dalton HP,
et al.
(1981) The diagnosis of strep throat in adults in the emergency room. Med Decis Making 1(3):239–246.
OpenUrl FREE Full Text

[14] Centor RM,

[15] Witherspoon JM,

[16] Dalton HP,

[17] et al.

[18] 4.↵
Singh S,
Dolan JG,
Centor RM
(2006) Optimal management of adults with pharyngitis – a multi-criteria decision analysis. BMC Med Inform Decis Mak 6:14.
OpenUrl PubMed

[19] Singh S,

[20] Dolan JG,

[21] Centor RM

[22] 5.↵
Lang ES,
Wyer PC,
Haynes RB
(2007) Knowledge translation: closing the evidence-to-practice gap. Ann Emerg Med 49(3):355–363.
OpenUrl CrossRef PubMed

[23] Lang ES,

[24] Wyer PC,

[25] Haynes RB

[26] 6.↵
Reilly BM,
Evans AT
(2006) Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med 144(3):201–209.
OpenUrl CrossRef PubMed

[27] Reilly BM,

[28] Evans AT

[29] 7.↵
Keogh C,
Fahey T
(2010) Clinical Prediction Rules in Primary Care: what can be done to maximise their implementation? Clinical Evidence, http://clinicalevidence.bmj.com/x/mce/file/05-10-10.pdf (accessed 26 Feb 2014).

[30] Keogh C,

[31] Fahey T

[32] 8.↵
Keogh C,
Wallace E,
O’Brien KK,
et al.
Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish a Web-based register. J Clin Epidemiol 64(8):848–860.

[33] Keogh C,

[34] Wallace E,

[35] O’Brien KK,

[36] et al.

[37] 9.↵
Toll DB,
Janssen KJ,
Vergouwe Y,
Moons KG
(2008) Validation, updating and impact of clinical prediction rules: a review. J Clin Epidemiol 61(11):1085–1094.
OpenUrl CrossRef PubMed

[38] Toll DB,

[39] Janssen KJ,

[40] Vergouwe Y,

[41] Moons KG

[42] 10.↵
Laupacis A,
Sekar N,
Stiell IG
(1997) Clinical prediction rules. A review and suggested modifications of methodological standards. JAMA 277(6):488–494.
OpenUrl CrossRef PubMed

[43] Laupacis A,

[44] Sekar N,

[45] Stiell IG

[46] 11.↵
Cox JL,
Holden JM,
Sagovsky R
(1987) Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. Br J Psychiatry 150:782–786.
OpenUrl Abstract/FREE Full Text

[47] Cox JL,

[48] Holden JM,

[49] Sagovsky R

[50] 12.↵
Folstein MF,
Folstein SE,
McHugh PR
(1975) ‘Mini-mental state’. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12(3):189–198.
OpenUrl CrossRef PubMed

[51] Folstein MF,

[52] Folstein SE,

[53] McHugh PR

[54] 13.↵
Kanis JA,
Johnell O,
Oden A,
et al.
(2008) FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int 19(4):385–397.
OpenUrl CrossRef PubMed

[55] Kanis JA,

[56] Johnell O,

[57] Oden A,

[58] et al.

[59] 14.↵
Tannenbaum M
Gleason DF
(1977) in Urologic pathology: the prostate, The Veteran’s Administration Cooperative Urologic Research Group: histologic grading and clinical staging of prostatic carcinoma, ed Tannenbaum M (Lea and Febiger, Phildelphia, PA), pp 171–198.

[60] Tannenbaum M

[61] Gleason DF

[62] 15.↵
Dukes C
(1932) The classification of cancer of the rectum. J Pathol Bacteriol 35:323–332.
OpenUrl CrossRef

[63] Dukes C

[64] 16.↵
Denoix PF
(1946) Enquete permanent dans les centres anticancereaux. [Continuous survey in cancer treatment centres] Bull Inst Nat Hyg 1:70–75.
OpenUrl

[65] Denoix PF

[66] 17.↵
Lim WS,
van der Eerden MM,
Laing R,
et al.
(2003) Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax 58(5):377–382.
OpenUrl Abstract/FREE Full Text

[67] Lim WS,

[68] van der Eerden MM,

[69] Laing R,

[70] et al.

[71] 18.↵
Knaus WA,
Draper EA,
Wagner DP,
Zimmerman JE
(1985) APACHE II: a severity of disease classification system. Crit Care Med 13(10):818–829.
OpenUrl CrossRef PubMed

[72] Knaus WA,

[73] Draper EA,

[74] Wagner DP,

[75] Zimmerman JE

[76] 19.↵
Teasdale G,
Jennett B
(1974) Assessment of coma and impaired consciousness. A practical scale. Lancet 2(7872):81–84.
OpenUrl CrossRef PubMed

[77] Teasdale G,

[78] Jennett B

[79] 20.↵
Barry MJ,
Fowler FJ Jr.,
O’Leary MP,
et al.
(1992) The American Urological Association symptom index for benign prostatic hyperplasia. The Measurement Committee of the American Urological Association. J Urol 148(5):1549–1557.
OpenUrl PubMed

[80] Barry MJ,

[81] Fowler FJ Jr.,

[82] O’Leary MP,

[83] et al.

[84] 21.
Hippisley-Cox J,
Coupland C,
Vinogradova Y,
et al.
(2007) Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ 335(7611):136.
OpenUrl Abstract/FREE Full Text

[85] Hippisley-Cox J,

[86] Coupland C,

[87] Vinogradova Y,

[88] et al.

[89] 22.
(2005) JBS 2: Joint British Societies’ guidelines on prevention of cardiovascular disease in clinical practice. Heart 91(Suppl 5):v1–52.
OpenUrl FREE Full Text

[90] 23.
Jackson R
(2000) Updated New Zealand cardiovascular disease risk-benefit prediction guide. BMJ 320(7236):709–710.
OpenUrl FREE Full Text

[91] Jackson R

[92] 24.
Haq IU,
Jackson PR,
Yeo WW,
Ramsay LE
(1995) Sheffield risk and treatment table for cholesterol lowering for primary prevention of coronary heart disease. Lancet 346(8988):1467–1471.
OpenUrl CrossRef PubMed

[93] Haq IU,

[94] Jackson PR,

[95] Yeo WW,

[96] Ramsay LE

[97] 25.
Anderson KM,
Wilson PW,
Odell PM,
Kannel WB
(1991) An updated coronary risk profile. A statement for health professionals. Circulation 83(1):356–362.
OpenUrl FREE Full Text

[98] Anderson KM,

[99] Wilson PW,

[100] Odell PM,

[101] Kannel WB

[102] 26.
Conroy RM,
Pyorala K,
Fitzgerald AP,
et al.
(2003) Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 24(11):987–1003.
OpenUrl Abstract/FREE Full Text

[103] Conroy RM,

[104] Pyorala K,

[105] Fitzgerald AP,

[106] et al.

[107] 27.
Assmann G,
Cullen P,
Schulte H
(2002) Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Munster (PROCAM) study. Circulation 105(3):310–315.
OpenUrl Abstract/FREE Full Text

[108] Assmann G,

[109] Cullen P,

[110] Schulte H

[111] 28.
Kroenke K,
Spitzer RL,
Williams JB
(2003) The Patient Health Questionnaire-2: validity of a two-item depression screener. Med Care 41(11):1284–1292.
OpenUrl CrossRef PubMed

[112] Kroenke K,

[113] Spitzer RL,

[114] Williams JB

[115] 29.
Kroenke K,
Spitzer RL,
Williams JB
(2001) The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 16(9):606–613.
OpenUrl CrossRef PubMed

[116] Kroenke K,

[117] Spitzer RL,

[118] Williams JB

[119] 30.
Spitzer RL,
Kroenke K,
Williams JB,
Lowe B
(2006) A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med 166(10):1092–1097.
OpenUrl CrossRef PubMed

[120] Spitzer RL,

[121] Kroenke K,

[122] Williams JB,

[123] Lowe B

[124] 31.
Zigmond AS,
Snaith RP
(1983) The hospital anxiety and depression scale. Acta Psychiatr Scand 67(6):361–370.
OpenUrl CrossRef PubMed

[125] Zigmond AS,

[126] Snaith RP

[127] 32.
Beck AT,
Steer RA,
Ball R,
Ranieri W
(1996) Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. J Pers Assess 67(3):588–597.
OpenUrl CrossRef PubMed

[128] Beck AT,

[129] Steer RA,

[130] Ball R,

[131] Ranieri W

[132] 33.
Stiell IG,
Greenberg GH,
McKnight RD,
et al.
(1993) Decision rules for the use of radiography in acute ankle injuries. Refinement and prospective validation. JAMA 269(9):1127–1132.
OpenUrl CrossRef PubMed

[133] Stiell IG,

[134] Greenberg GH,

[135] McKnight RD,

[136] et al.

[137] 34.
Stiell IG,
Greenberg GH,
Wells GA,
et al.
(1995) Derivation of a decision rule for the use of radiography in acute knee injuries. Ann Emerg Med 26(4):405–413.
OpenUrl CrossRef PubMed

[138] Stiell IG,

[139] Greenberg GH,

[140] Wells GA,

[141] et al.

[142] 35.
Seaberg DC,
Jackson R
(1994) Clinical decision rule for knee radiographs. Am J Emerg Med 12(5):541–543.
OpenUrl CrossRef PubMed

[143] Seaberg DC,

[144] Jackson R

[145] 36.
Gail MH,
Brinton LA,
Byar DP,
et al.
(1989) Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 81(24):1879–1886.
OpenUrl Abstract/FREE Full Text

[146] Gail MH,

[147] Brinton LA,

[148] Byar DP,

[149] et al.

[150] 37.
Coulson AS,
Glasspool DW,
Fox J,
Emery J
(2001) RAGs: a novel approach to computerized genetic risk assessment and decision support from pedigrees. Methods Inf Med 40(4):315–322.
OpenUrl PubMed

[151] Coulson AS,

[152] Glasspool DW,

[153] Fox J,

[154] Emery J

[155] 38.
McCarthy PL,
Sharpe MR,
Spiesel SZ,
et al.
(1982) Observation scales to identify serious illness in febrile children. Pediatrics 70(5):802–809.
OpenUrl Abstract/FREE Full Text

[156] McCarthy PL,

[157] Sharpe MR,

[158] Spiesel SZ,

[159] et al.

[160] 39.
NICE. Feverish illness in children (CG47) (replaced by CG160)
Feverish illness in children — Assessment and initial management in children younger than 5 years, http://guidance.nice.org.uk/CG47 (accessed 26 Feb 2014).

[161] NICE. Feverish illness in children (CG47) (replaced by CG160)

[162] 40.
McIsaac WJ,
Kellner JD,
Aufricht P,
et al.
(2004) Empirical validation of guidelines for the management of pharyngitis in children and adults. JAMA 291(13):1587–1595.
OpenUrl CrossRef PubMed

[163] McIsaac WJ,

[164] Kellner JD,

[165] Aufricht P,

[166] et al.

[167] 41.
Rothwell PM,
Giles MF,
Flossmann E,
et al.
(2005) A simple score (ABCD) to identify individuals at high early risk of stroke after transient ischaemic attack. Lancet 366(9479):29–36.
OpenUrl CrossRef PubMed

[168] Rothwell PM,

[169] Giles MF,

[170] Flossmann E,

[171] et al.

[172] 42.
Johnston SC,
Gress DR,
Browner WS,
Sidney S
(2001) Short-term prognosis after emergency department diagnosis of TIA. JAMA 284(22):2901–2906.
OpenUrl

[173] Johnston SC,

[174] Gress DR,

[175] Browner WS,

[176] Sidney S

[177] 43.
Gage BF,
Waterman AD,
Shannon W,
et al.
(2001) Validation of clinical classification schemes for predicting stroke: results from the National Registry of Atrial Fibrillation. JAMA 285(22):2864–2870.
OpenUrl CrossRef PubMed

[178] Gage BF,

[179] Waterman AD,

[180] Shannon W,

[181] et al.

[182] 44.
Wells PS,
Hirsh J,
Anderson DR,
et al.
(1998) A simple clinical model for the diagnosis of deep-vein thrombosis combined with impedance plethysmography: potential for an improvement in the diagnostic process. J Intern Med 243(1):15–23.
OpenUrl CrossRef PubMed

[183] Wells PS,

[184] Hirsh J,

[185] Anderson DR,

[186] et al.

[187] 45.
Wells PS,
Anderson DR,
Rodger M,
et al.
(2000) Derivation of a simple clinical model to categorize patients probability of pulmonary embolism: increasing the models utility with the SimpliRED D-dimer. Thromb Haemost 83(3):416–420.
OpenUrl PubMed

[188] Wells PS,

[189] Anderson DR,

[190] Rodger M,

[191] et al.

[192] 46.
Kothari V,
Stevens RJ,
Adler AI,
et al.
(2002) UKPDS 60: risk of stroke in type 2 diabetes estimated by the UK Prospective Diabetes Study risk engine. Stroke 33(7):1776–1781.
OpenUrl Abstract/FREE Full Text

[193] Kothari V,

[194] Stevens RJ,

[195] Adler AI,

[196] et al.

[197] 47.
(2003) 2003 European Society of Hypertension-European Society of Cardiology guidelines for the management of arterial hypertension. J Hypertens 21(6):1011–1053.
OpenUrl CrossRef PubMed

[198] 48.
Grover SA,
Abrahamowicz M,
Joseph L,
et al.
(1992) The benefits of treating hyperlipidemia to prevent coronary heart disease. Estimating changes in life expectancy and morbidity. JAMA 267(6):816–822.
OpenUrl CrossRef PubMed

[199] Grover SA,

[200] Abrahamowicz M,

[201] Joseph L,

[202] et al.

[203] 49.
Ridker PM,
Buring JE,
Rifai N,
Cook NR
(2007) Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA 297(6):611–619.
OpenUrl CrossRef PubMed

[204] Ridker PM,

[205] Buring JE,

[206] Rifai N,

[207] Cook NR

[208] 50.
Woodward M,
Brindle P,
TunstallĐPedoe H
(2007) Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC) Heart 93(2):172–176.
OpenUrl Abstract/FREE Full Text

[209] Woodward M,

[210] Brindle P,

[211] TunstallĐPedoe H

[212] 51.
Chalmers J,
MacMahon S,
Mancia G,
et al.
(1999) 1999 World Health Organization - International Society of Hypertension Guidelines for the management of hypertension. Guidelines sub-committee of the WHO. Clin Exp Hypertens 21(5–6):1009–1060.
OpenUrl CrossRef PubMed

[213] Chalmers J,

[214] MacMahon S,

[215] Mancia G,

[216] et al.

[217] 52.
Johnston SC,
Rothwell PM,
Nguyen-Huynh MN,
et al.
(2007) Validation and refinement of scores to predict very early stroke risk after transient ischaemic attack. Lancet 369(9558):283–292.
OpenUrl CrossRef PubMed

[218] Johnston SC,

[219] Rothwell PM,

[220] Nguyen-Huynh MN,

[221] et al.

[222] 53.
Schnabel RB,
Sullivan LM,
Levy D,
et al.
(2009) Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet 373(9665):739–745.
OpenUrl CrossRef PubMed

[223] Schnabel RB,

[224] Sullivan LM,

[225] Levy D,

[226] et al.

[227] 54.
Harbison J,
Hossain O,
Jenkinson D,
et al.
(2003) Diagnostic accuracy of stroke referrals from primary care, emergency room physicians, and ambulance staff using the face arm speech test. Stroke 34(1):71–76.
OpenUrl Abstract/FREE Full Text

[228] Harbison J,

[229] Hossain O,

[230] Jenkinson D,

[231] et al.

[232] 55.
Nor AM,
Davis J,
Sen B,
et al.
The Recognition of Stroke in the Emergency Room (ROSIER) scale: development and validation of a stroke recognition instrument. Lancet Neurol 4(11):727–734.

[233] Nor AM,

[234] Davis J,

[235] Sen B,

[236] et al.

[237] 56.
Bray JE,
Martin J,
Cooper G,
et al.
(2005) Paramedic identification of stroke: community validation of the Melbourne ambulance stroke screen. Cerebrovasc Dis 20(1):28–33.
OpenUrl CrossRef PubMed

[238] Bray JE,

[239] Martin J,

[240] Cooper G,

[241] et al.

[242] 57.
Goldberg D,
Williams P
(1988) A user’s guide to the General Health Questionnaire (NFER-Nelson, Basingstoke).

[243] Goldberg D,

[244] Williams P

[245] 58.
Sutcliffe LM,
Lincoln NB
(1998) The assessment of depression in aphasic stroke patients: the development of the Stroke Aphasic Depression Questionnaire. Clin Rehabil 12(6):506–513.
OpenUrl Abstract/FREE Full Text

[246] Sutcliffe LM,

[247] Lincoln NB

[248] 59.
Malnutrition Advisory Group
(2000) MAG guidelines for detection and management of malnutrition (British Association for Parenteral and Enteral Nutrition (BAPEN) Maidenhead).

[249] Malnutrition Advisory Group

[250] 60.
Heikes KE,
Eddy DM,
Arondekar B,
Schlessinger L
(2008) Diabetes Risk Calculator: a simple tool for detecting undiagnosed diabetes and pre-diabetes. Diabetes Care 31(5):1040–1045.
OpenUrl Abstract/FREE Full Text

[251] Heikes KE,

[252] Eddy DM,

[253] Arondekar B,

[254] Schlessinger L

[255] 61.
Lindstrom J,
Tuomilehto J
(2003) The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 26(3):725–731.
OpenUrl Abstract/FREE Full Text

[256] Lindstrom J,

[257] Tuomilehto J

[258] 62.
Cadarette SM,
Jaglal SB,
Kreiger N,
et al.
(2000) Development and validation of the Osteoporosis Risk Assessment Instrument to facilitate selection of women for bone densitometry. CMAJ 162(9):1289–1294.
OpenUrl Abstract/FREE Full Text

[259] Cadarette SM,

[260] Jaglal SB,

[261] Kreiger N,

[262] et al.

[263] 63.
Lydick E,
Cook K,
Turpin J,
et al.
(1998) Development and validation of a simple questionnaire to facilitate identification of women likely to have low bone density. Am J Manag Care 4(1):37–48.
OpenUrl PubMed

[264] Lydick E,

[265] Cook K,

[266] Turpin J,

[267] et al.

[268] 64.
Lowery DJ,
Farley TD,
Wing DW,
et al.
(2006) A clinical composite score accurately detects meniscal pathology. Arthroscopy 22(11):1174–1179.
OpenUrl PubMed

[269] Lowery DJ,

[270] Farley TD,

[271] Wing DW,

[272] et al.

[273] 65.
de Bie RA,
de Vet HC,
van den Wildenberg FA,
et al.
(1997) The prognosis of ankle sprains. Int J Sports Med 18(4):285–289.
OpenUrl PubMed

[274] de Bie RA,

[275] de Vet HC,

[276] van den Wildenberg FA,

[277] et al.

[278] 66.
Fine MJ,
Auble TE,
Yealy DM,
et al.
(1997) A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med 336(4):243–250.
OpenUrl CrossRef PubMed

[279] Fine MJ,

[280] Auble TE,

[281] Yealy DM,

[282] et al.

[283] 67.
(1991) WHO guidelines on detecting pneumonia in children. Lancet 338(8780):1453–1454.
OpenUrl PubMed

[284] 68.
Dubos F,
Moulin F,
Raymond J,
et al.
(2007) [Distinction between bacterial and aseptic meningitis in children: refinement of a clinical decision rule] Arch Pediatr 14(5):434–438.
OpenUrl CrossRef PubMed

[285] Dubos F,

[286] Moulin F,

[287] Raymond J,

[288] et al.

[289] 69.
Nigrovic LE,
Kuppermann N,
Malley R
(2002) Development and validation of a multivariable predictive model to distinguish bacterial from aseptic meningitis in children in the post-Haemophilus influenzae era. Pediatrics 110(4):712–719.
OpenUrl Abstract/FREE Full Text

[290] Nigrovic LE,

[291] Kuppermann N,

[292] Malley R

[293] 70.
Hoen B,
Viel JF,
Paquot C,
et al.
(1995) Multivariate approach to differential diagnosis of acute meningitis. Eur J Clin Microbiol Infect Dis 14(4):267–274.
OpenUrl CrossRef PubMed

[294] Hoen B,

[295] Viel JF,

[296] Paquot C,

[297] et al.

[298] 71.
Sinclair JF,
Skeoch CH,
Hallworth D
(1987) Prognosis of meningococcal septicaemia. Lancet 2(8549):38.
OpenUrl PubMed

[299] Sinclair JF,

[300] Skeoch CH,

[301] Hallworth D

[302] 72.
Chen J,
Pee D,
Ayyagari R,
et al.
(2006) Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. J Natl Cancer Inst 98(17):1215–1226.
OpenUrl Abstract/FREE Full Text

[303] Chen J,

[304] Pee D,

[305] Ayyagari R,

[306] et al.

[307] 73.
Claus EB,
Risch N,
Thompson WD
(1991) Genetic analysis of breast cancer in the cancer and steroid hormone study. Am J Hum Genet 48(2):232–242.
OpenUrl PubMed

[308] Claus EB,

[309] Risch N,

[310] Thompson WD

[311] 74.
Parmigiani G,
Berry D,
Aguilar O
(1998) Determining carrier probabilities for breast cancer-susceptibility genes BRCA1 and BRCA2. Am J Hum Genet 62(1):145–158.
OpenUrl CrossRef PubMed

[312] Parmigiani G,

[313] Berry D,

[314] Aguilar O

[315] 75.
Glas AM,
Floore A,
Delahaye LJ,
et al.
(2006) Converting a breast cancer microarray signature into a high-throughput diagnostic test. BMC Genomics 7:278.
OpenUrl CrossRef PubMed

[316] Glas AM,

[317] Floore A,

[318] Delahaye LJ,

[319] et al.

[320] 76.
Paik S,
Shak S,
Tang G,
et al.
(2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351(27):2817–2826.
OpenUrl CrossRef PubMed

[321] Paik S,

[322] Shak S,

[323] Tang G,

[324] et al.

[325] 77.
Ravdin PM,
Siminoff LA,
Davis GJ,
et al.
(2001) Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol 19(4):980–991.
OpenUrl Abstract/FREE Full Text

[326] Ravdin PM,

[327] Siminoff LA,

[328] Davis GJ,

[329] et al.

[330] 78.
Blamey RW
(1996) The design and clinical use of the Nottingham Prognostic Index in breast cancer. Breast 5:156–157.
OpenUrl CrossRef

[331] Blamey RW

[332] 79.
Evans DG,
Eccles DM,
Rahman N,
et al.
(2004) A new scoring system for the chances of identifying a BRCA1/2 mutation outperforms existing models including BRCAPRO. J Med Genet 41(6):474–480.
OpenUrl Abstract/FREE Full Text

[333] Evans DG,

[334] Eccles DM,

[335] Rahman N,

[336] et al.

[337] 80.
Gilpin CA,
Carson N,
Hunter AG
(2000) A preliminary validation of a family history assessment form to select women at risk for breast or ovarian cancer for referral to a genetics center. Clin Genet 58(4):299–308.
OpenUrl CrossRef PubMed

[338] Gilpin CA,

[339] Carson N,

[340] Hunter AG

[341] 81.
Tyrer J,
Duffy SW,
Cuzick J
(2004) A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 23(7):1111–1130.
OpenUrl CrossRef PubMed

[342] Tyrer J,

[343] Duffy SW,

[344] Cuzick J

[345] 82.
Hamilton M
(1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23:56–62.
OpenUrl FREE Full Text

[346] Hamilton M

[347] 83.
Montgomery SA,
Asberg M
(1979) A new depression scale designed to be sensitive to change. Br J Psychiatry 134:382–389.
OpenUrl Abstract/FREE Full Text

[348] Montgomery SA,

[349] Asberg M

[350] 84.
Rush AJ,
Trivedi MH,
Ibrahim HM,
et al.
(2003) The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry 54(5):573–583.
OpenUrl CrossRef PubMed

[351] Rush AJ,

[352] Trivedi MH,

[353] Ibrahim HM,

[354] et al.

[355] 85.
Alexopoulos GS,
Abrams RC,
Young RC,
Shamoian CA
(1988) Cornell Scale for Depression in Dementia. Biol Psychiatry 23(3):271–284.
OpenUrl CrossRef PubMed

[356] Alexopoulos GS,

[357] Abrams RC,

[358] Young RC,

[359] Shamoian CA

[360] 86.
Bech P,
Rafaelsen OJ
(1980) The use of rating scales exemplified by a comparison of the Hamilton and the Bech-Rafaelsen Melancholia Scale. Acta Psychiatr Scand 62(Suppl 285):128–132.
OpenUrl CrossRef

[361] Bech P,

[362] Rafaelsen OJ

[363] 87.
Guy W
(1976) ECDEU Assessment Manual for Psychopharmacology — Revised (Department of Health, Education, and Welfare, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, NIMH Psychopharmacology Research Branch, Division of Extramural Research Programs, Rockville, MD) (DHEW Publ No ADM 76–338).

[364] Guy W

[365] 88.
Yesavage JA,
Brink TL,
Rose TL,
et al.
(1982) Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res 17(1):37–49.
OpenUrl CrossRef PubMed

[366] Yesavage JA,

[367] Brink TL,

[368] Rose TL,

[369] et al.

[370] 89.
Radloff LS
(1977) The CES-D scale: A self-report depression scale for research in the general population. Appl Psycholog Measure 1:385–401.
OpenUrl Abstract

[371] Radloff LS

[372] 90.
Fleming M,
Barry K
Brown RL
(1992) in Addictive disorders, Identification and office management of alcohol and drug disorders, eds Fleming M, Barry K (Mosby, St. Louis, MO), pp 25–43.

[373] Fleming M,

[374] Barry K

[375] Brown RL

[376] 91.↵
Beswick AD,
Brindle P,
Fahey T,
Ebrahim S
(May, 2008) NICE Clinical Guidelines, No. 67S, A Systematic Review of Risk Scoring Methods and Clinical Decision Aids Used in the Primary Prevention of Coronary Heart Disease (Supplement) (Royal College of General Practitioners (UK) London).

[377] Beswick AD,

[378] Brindle P,

[379] Fahey T,

[380] Ebrahim S

[381] 92.
Dallongeville J,
Banegas JR,
Tubach F,
et al.
(2012) Survey of physicians’ practices in the control of cardiovascular risk factors: the EURIKA study. Eur J Prev Cardiol 19(3):541–550.
OpenUrl Abstract/FREE Full Text

[382] Dallongeville J,

[383] Banegas JR,

[384] Tubach F,

[385] et al.

[386] 93.
Porche K,
Reymond L,
Callaghan JO,
Charles M
(2014) Depression in palliative care patients: a survey of assessment and treatment practices of Australian and New Zealand palliative care specialists. Aust Health Rev 38(1):44–50.
OpenUrl

[387] Porche K,

[388] Reymond L,

[389] Callaghan JO,

[390] Charles M

[391] 94.↵
Edwards QT,
Maradiegue A,
Seibert D,
et al.
(2009) Breast cancer risk elements and nurse practitioners’ knowledge, use, and perceived comfort level of breast cancer risk assessment. J Am Acad Nurse Pract 21(5):270–277.
OpenUrl PubMed

[392] Edwards QT,

[393] Maradiegue A,

[394] Seibert D,

[395] et al.

Main menu

User menu

Search

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

Abstract

INTRODUCTION

How this fits in

METHOD

Review of clinical guidelines

Search strategy

Study selection

Data extraction

Survey of GPs

Participants

Survey

RESULTS

Review of clinical guidelines

Survey of UK GPs

DISCUSSION

Summary

Strengths and limitations

Comparison with existing literature

Implications for research and practice

Acknowledgments

Appendix

Notes

Funding

Ethical approval

Provenance

Competing interests

Discuss this article

Footnotes

REFERENCES

In this issue

Citation Manager Formats

Jump to section

Keywords

More in this TOC Section

Related Articles

Cited By...

NAVIGATE

RCGP

MY ACCOUNT

NEWS AND UPDATES

AUTHORS & REVIEWERS

CUSTOMER SERVICES

CONTRIBUTE

CONTACT US