Development of a clinical prediction model to calculate patient life expectancy: the measure of actuarial life expectancy (male)
OBJECTIVES: To develop a clinical prediction model enabling the calculation of an individual patient's life expectancy (LE) and survival probability based on age, sex, and comorbidity for use in the joint decision-making process regarding medical treatment. METHODS: A computer software program was d...
| Published in: | Medical Decision Making Vol. 29; no. 2; pp. 239 - 247 |
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| Main Authors: | , , |
| Format: | research Journal Article |
| Published: |
Sage Publications Inc.
Mar/Apr2009
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105501209&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105501209 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0272989X DKI jtl: Medical Decision Making issn: 0272989X maglogo: Y pubinfo: dt: Mar/Apr2009 vid: 29 iid: 2 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 105501209 105501209 2010248940 10.1177/0272989X08327114 NLM19047762 105501209 ppf: 239 ppct: 8 formats: tig: atl: Development of a clinical prediction model to calculate patient life expectancy: the measure of actuarial life expectancy (male) aug: au: Clarke MG Kennedy KP Macdonagh RP affil: Department of General Surgery & Urology, Taunton & Somerset Hospital, Musgrove Park, Taunton, UK. mgclarkey@doctors.org.uk. sug: subj: Decision Support Techniques Life Expectancy Mathematics Age Factors Comorbidity Models, Statistical Sex Factors Software Design Survival Analysis Human ab: OBJECTIVES: To develop a clinical prediction model enabling the calculation of an individual patient's life expectancy (LE) and survival probability based on age, sex, and comorbidity for use in the joint decision-making process regarding medical treatment. METHODS: A computer software program was developed with a team of 3 clinicians, 2 professional actuaries, and 2 professional computer programmers. This incorporated statistical spreadsheet and database access design methods. Data sources included life insurance industry actuarial rating factor tables (public and private domain), Government Actuary Department UK life tables, professional actuarial sources, and evidence-based medical literature. The main outcome measures were numerical and graphical display of comorbidity-adjusted LE; 5-, 10-, and 15-year survival probability; in addition to generic UK population LE. RESULTS: Nineteen medical conditions, which impacted significantly on LE in actuarial terms and were commonly encountered in clinical practice, were incorporated in the final model. Numerical and graphical representations of statistical predictions of LE and survival probability were successfully generated for patients with either no comorbidity or a combination of the 19 medical conditions included. Validation and testing, including actuarial peer review, confirmed consistency with the data sources utilized. CONCLUSIONS: The evidence-based actuarial data utilized in this computer program design represent a valuable resource for use in the clinical decision-making process, where an accurate objective assessment of patient LE can so often make the difference between patients being offered or denied medical and surgical treatment. Ongoing development to incorporate additional comorbidities and enable Web-based access will enhance its use further. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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