Predicting poorer health outcomes in older community-dwelling patients with multimorbidity: prospective cohort study assessing the accuracy of different multimorbidity definitions
Bibliographic record
Abstract
PURPOSE: Multimorbidity is commonly defined and measured using condition counts. The UK National Institute for Health Care Excellence Guidelines for Multimorbidity suggest that a medication-orientated approach could be used to identify those in need of a multimorbidity approach to management. OBJECTIVES: To compare the accuracy of medication-based and diagnosis-based multimorbidity measures at higher cut-points to identify older community-dwelling patients who are at risk of poorer health outcomes. DESIGN: A secondary analysis of a prospective cohort study with a 2-year follow-up (2010-2012). SETTING: 15 general practices in Ireland. PARTICIPANTS: 904 older community-dwelling patients. EXPOSURE: Baseline multimorbidity measurements based on both medication classes count (MCC) and chronic disease count (CDC). OUTCOMES: Mortality, self-reported health related quality of life, mental health and physical functioning at follow-up. ANALYSIS: Sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) adjusting for clustering by practice for each outcome using both definitions. RESULTS: Of the 904 baseline participants, 53 died during follow-up and 673 patients completed the follow-up questionnaire. At baseline, 223 patients had 3 or more chronic conditions and 89 patients were prescribed 10 or more medication classes. Sensitivity was low for both MCC and CDC measures for all outcomes. For specificity, MCC was better for all outcomes with estimates varying from 88.8% (95% CI 85.2% to 91.6%) for physical functioning to 90.9% (95% CI 86.2% to 94.1%) for self-reported health-related quality of life. There were no differences between MCC and CDC in terms of PPV and NPV for any outcomes. CONCLUSIONS: Neither measure demonstrated high sensitivity. However, MCC using a definition of 10 or more regular medication classes to define multimorbidity had higher specificity for predicting poorer health outcomes. While having limitations, this definition could be used for proactive identification of patients who may benefit from targeted clinical care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".