| Course | HCR 400 Evidence-Based Practice for the Health Care Professional |
|---|---|
| Module | Module 3 |
| Paper type | Critical appraisal of a prognostic study |
| Length | About 503 words, 4 pages |
| Format | APA 7 student paper |
| School | Arizona State University |
| Program | BS in Health Care Coordination |
| Updated | October 2026 |
Free sample paper for HCR 400 Module 3
Rapid Critical Appraisal: The Charlson Comorbidity Index and Prognosis
Student Name
BS in Health Care Coordination, Arizona State University
HCR 400: Evidence-Based Practice for the Health Care Professional
Instructor Name
Month Day, Year
Rapid Critical Appraisal: The Charlson Comorbidity Index and Prognosis
Study and Question
Study: Charlson et al. (1987), development and validation of a comorbidity index.
Prognostic question: In hospitalized medical patients (P), does the number and seriousness of comorbid conditions, summarized in a weighted index (exposure), predict risk of death (O) over one year and longer (T)?
Are the Results Valid?
The use of a separate validation cohort is a strength, since prognostic scores often perform worse outside the data that created them. The main limitations are the small numbers with any single condition, which the authors acknowledge, and the single-center origin (Hoffmann et al., 2023).
| Appraisal question | Answer | Notes |
|---|---|---|
| Was there a representative sample assembled at a common point? | Partly | 559 medical patients admitted to one hospital service; development cohort from one center |
| Was follow-up long and complete enough? | Yes for its aims | One year in the development cohort; ten years in the validation cohort |
| Were outcomes defined objectively and applied without bias? | Mostly | Death is objective; attributing death to comorbid disease requires judgment |
| Was the model validated in a separate group? | Yes | Tested in 685 women with breast cancer followed for ten years |
| Were other prognostic factors considered? | Partly | Age was a predictor in the longer follow-up |
What Are the Results?
In the development cohort, one-year mortality rose with the index: 12 percent for a score of 0, 26 percent for 1 to 2, 52 percent for 3 to 4 and 85 percent for 5 or more. In the validation cohort, deaths attributed to comorbid disease over ten years were 8 percent for a score of 0, 25 percent for 1, 48 percent for 2 and 59 percent for 3 or more, a stepwise increase that was statistically significant (Charlson et al., 1987). The precision of each estimate is limited by small group sizes; for example, only 18 women had scores of 3 or more.
Will the Results Help My Patients?
The index has become one of the most widely used comorbidity measures in health services research. For a care coordinator, it offers a simple way to identify patients whose other illnesses place them at high risk and who may benefit from more intensive follow-up or advance care planning conversations. But the original weights came from 1980s treatment, and survival for conditions such as HIV and some cancers has changed greatly, so later adaptations and local validation matter. The index predicts risk for groups and should support, not replace, individual clinical judgment. Risk stratification of this kind is one of the activities associated with population health management, although definitions of that field vary (Steenkamer et al., 2017).
Appraisal Summary
| Domain | Judgment |
|---|---|
| Validity | Reasonable, with external validation; small subgroups |
| Results | Clear stepwise relationship between score and mortality |
| Applicability | Useful for risk stratification; weights may need updating |
Conclusion
The original Charlson study offers valid evidence that a weighted count of comorbid conditions predicts mortality. Its continued use supports applying the index for risk stratification, with awareness that its weights reflect treatment of the time.
References
Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases, 40(5), 373-383. https://doi.org/10.1016/0021-9681(87)90171-8
Hoffmann, T., Bennett, S., & Del Mar, C. (2023). Evidence-based practice across the health professions (4th ed.). Elsevier.
Steenkamer, B. M., Drewes, H. W., Heijink, R., Baan, C. A., & Struijs, J. N. (2017). Defining population health management: A scoping review of the literature. Population Health Management, 20(1), 74-85. https://doi.org/10.1089/pop.2015.0149
Reading the HCR 400 Module 3 assignment instructions
The prognosis appraisal is worth 15 of the 85 appraisal points in HCR 400. Prognostic research asks what is likely to happen to people with a given condition or characteristic as time passes, which is why the appraisal turns to how the cohort was gathered and from what starting point, how long and how completely it was followed, how outcomes were recorded and whether any scoring tool was checked in a fresh group of patients. The textbook's chapter on prognosis supplies these questions. Cohort studies and papers that build or test risk scores both suit this assignment if you are choosing your own. Report findings the way the authors present them, whether as survival rates, risk by category or hazard ratios, and comment on how precise each estimate is.
Inside the HCR 400 Module 3 example
Opening with the original study and a prognostic question that names population, exposure, outcome and time frame, the sample appraises validity in a table covering sampling and starting point, follow-up, outcome definitions, external validation and other predictors. A short paragraph then names the separate validation cohort as the study's main strength and the thin numbers in each score band, which the authors themselves acknowledged, as its main weakness. Results for both cohorts are laid out with an explicit comment on how few patients sat in the highest categories. The applicability section explains how care coordinators use comorbidity scores for risk stratification today and why weights set in the 1980s may need updating.
Where the marks sit in the HCR 400 Module 3 rubric
Fifteen points ride on the prognosis appraisal under the Canvas rubric. Good work generally earns credit for describing the cohort and its starting point accurately, judging whether follow-up was long and complete enough, examining how outcomes were defined, checking whether any prediction tool was validated outside its development data, reporting results with attention to their precision and arguing whether the findings still hold for current patients. Common deductions come from applying trial criteria such as randomization to a cohort, ignoring confidence or sample size in each risk group and overlooking how much treatment has changed since the data were collected.
HCR 400 Module 3 help from the desk
Prognostic studies are nearly always cohorts, so judge them on cohort terms rather than criticizing them for lacking a control group. Check whether a score was tested in a population different from the one that produced it, since performance usually drops outside the original data. Count how many people fall into each category; small groups make estimates wobble. Ask whether care has changed enough to alter the predictions. If you need a prognostic study linked to your group's topic, the desk can suggest search terms that tend to find them. Finally, remember that a prognostic score describes groups; when you discuss applicability, explain how a clinician or coordinator would combine the score with knowledge of the individual patient.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Arizona State University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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HCR 400 Module 3 questions, answered
Where can I find a free HCR 400 Module 3 sample paper?
A full HCR 400 Module 3 sample is on this page: a rapid appraisal of the original Charlson comorbidity index study.
What is the Charlson comorbidity index?
A weighted score of a patient's comorbid conditions developed in 1987 to predict mortality risk.
How is a prognosis study appraised?
By examining the cohort, follow-up, outcome measurement, validation of any prediction tool and the precision of results.
Why validate a prediction score in a new group?
Scores often perform worse outside the data used to create them, so testing in a separate cohort shows whether they generalize.
How much is the HCR 400 prognosis appraisal worth?
It is worth 15 points.