5a. Patient information

What to write

5a – Demographic information of the patient (age, gender, ethnicity, occupation).

5b – Main symptoms of the patient (chief complaint).

5c – Medical, family, and psychosocial history—including lifestyle and genetic information whenever possible, details about relevant comorbidities, and past interventions, and their outcomes.

Explanation

We suggest including relevant demographic information about the patient while maintaining anonymity. Characteristics to identify the patient should ideally include age, sex and gender, race, and ethnicity—these characteristics may become important if many cases are subsequently reported. See the list below, from the US Department of Health and Human Services, of some personal identifiers that should not be used in a case report because they might reveal the patient’s identity.

When appropriate, include the patient’s own words about their chief complaint or symptoms that led to their initial visit. Specify how long symptoms have been present and if relevant, the frequency, intensity, location, and aggravating or alleviating factors. Distinguish comorbidities, when they began, whether they are recurring, past and current interventions and their outcomes. When discussing a history of allergies, include allergens, dates of reactions, and the type of allergic manifestation1.

Other historical factors may be relevant, such as:

  • Perinatal history, such as type of birth, length of pregnancy, if breast-fed, and for how long
  • Psychosocial history (e.g., occupation, social support, education level)
  • Type of health insurance
  • Environmental exposures (living and working environment, potential toxic exposures)
  • Lifestyle (sleep, stress management, exercise, recreational drug use, smoking, alcohol consumption, and nutrition/diet)
  • Family medical history (e.g., if family members have similar conditions as the patient)
  • Genetic information (relevant to the case)

Patient identifiers to be excluded

According to the US Department of Health and Human Services, the following personal identifiers should not be used in a case report because they might reveal the patient’s identity:

  • Names
  • Geographic regions
  • Elements of dates including birth date, date of death, and admission/discharge date
  • Listing ages older than 89 years require additional consent unless providing a single category of age >90 years
  • Telephone numbers, fax numbers, and e-mail addresses
  • Personal identifying numbers (e.g., social security numbers, medical record numbers)
  • Web Universal Resource Locators (URLs) and Internet Protocol (IP) addresses
  • Biometric identifiers, photographs and images (without specific additional permission)2,
  • Other unique, identifying characteristics or codes

source.

Examples

5a, 5b, and 5c—Patient information.

“Case Report: The proband is a male born in 1942. At the age of 19 years, he had his first episode of deep venous thrombosis in one leg. After this, he was healthy and free of thrombosis for almost 20 years. Between 1980 and 1987, he had multiple episodes of deep venous thrombosis, at least once a year. The thrombotic events were treated with vitamin K antagonists for periods of up to 3 months. The presence of a thrombus was verified with phlebography on at least two occasions. The proband has developed a postthrombotic syndrome in his legs but has no other disorders. Several members of the proband’s family have similar histories of multiple episodes of deep venous thrombosis (Fig. 1). His older brother by 10 years (III-2) has had deep venous thrombosis (in the legs) on several occasions, most of them occurring between the ages of 45 and 50 years. Also, his uncle (11-7) and aunt (II-5) have both had multiple episodes of thrombosis.

A younger relative (IV-2) had clinically suspected deep venous thrombosis during her third pregnancy, but phlebography failed technically. The proband’s father, who had no history of thrombosis, is deceased. Nineteen of the family members (all living members of generations II–IV) were available for testing. Two additional, unrelated cases with thrombophilia and inherited poor response to APC were identified; their medical histories are briefly described in the legend to Fig. 6.”

From Familial thrombophilia due to a previously unrecognized mechanism characterized by poor anticoagulant response to activated protein C: Prediction of a cofactor to activated protein C3.

“A 45-year-old African-American woman presented to the rheumatology clinic with a history of UCTD, manifesting as biopsy-proven urticarial dermatitis, inflammatory arthritis, fatigue, and weight loss in the setting of positive immunofluorescence antinuclear antibodies (1:160, speckled pattern), anti-RNP, anti-Sm/RNP, and antichromatin antibodies.”

From Quinacrine-induced cholestatic hepatitis in undifferentiated connective tissue disease (UCTD)4.

Training

The UK EQUATOR Centre runs training on how to write using reporting guidelines.

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References

1.
Cohen H. How to write a patient case report. American Journal of Health-System Pharmacy. 2006;63(19):1888-1892. doi:10.2146/ajhp060182
2.
Lang TA, Talerico C, Siontis GCM. Documenting clinical and laboratory images in publications. Chest. 2012;141(6):1626-1632. doi:10.1378/chest.11-1800
3.
Dahlbäck B, Carlsson M, Svensson PJ. Familial thrombophilia due to a previously unrecognized mechanism characterized by poor anticoagulant response to activated protein c: Prediction of a cofactor to activated protein c. Proceedings of the National Academy of Sciences. 1993;90(3):1004-1008. doi:10.1073/pnas.90.3.1004
4.
NAMAS R, MARQUARDT A. Case report and literature review: Quinacrine-induced cholestatic hepatitis in undifferentiated connective tissue disease. The Journal of Rheumatology. 2015;42(7):1354-1355. doi:10.3899/jrheum.150050

Citation

For attribution, please cite this work as:
Gagnier JJ, Kienle G, Altman DG, et al. The CARE reporting guideline for writing clinical case report articles. The EQUATOR Network guideline dissemination platform. doi:10.1234/equator/1010101

Reporting Guidelines are recommendations to help describe your work clearly

Your research will be used by people from different disciplines and backgrounds for decades to come. Reporting guidelines list the information you should describe so that everyone can understand, replicate, and synthesise your work.

Reporting guidelines do not prescribe how research should be designed or conducted. Rather, they help authors transparently describe what they did, why they did it, and what they found.

Reporting guidelines make writing research easier, and transparent research leads to better patient outcomes.

Easier writing

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Smoother publishing

Many journals require completed reporting checklists at submission.

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Maximum impact

From nobel prizes to null results, articles have more impact when everyone can use them.

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Who reads research?

You work will be read by different people, for different reasons, around the world, and for decades to come. Reporting guidelines help you consider all of your potential audiences. For example, your research may be read by researchers from different fields, by clinicians, patients, evidence synthesisers, peer reviewers, or editors. Your readers will need information to understand, to replicate, apply, appraise, synthesise, and use your work.

Cohort studies

A cohort study is an observational study in which a group of people with a particular exposure (e.g. a putative risk factor or protective factor) and a group of people without this exposure are followed over time. The outcomes of the people in the exposed group are compared to the outcomes of the people in the unexposed group to see if the exposure is associated with particular outcomes (e.g. getting cancer or length of life).

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Case-control studies

A case-control study is a research method used in healthcare to investigate potential risk factors for a specific disease. It involves comparing individuals who have been diagnosed with the disease (cases) to those who have not (controls). By analysing the differences between the two groups, researchers can identify factors that may contribute to the development of the disease.

An example would be when researchers conducted a case-control study examining whether exposure to diesel exhaust particles increases the risk of respiratory disease in underground miners. Cases included miners diagnosed with respiratory disease, while controls were miners without respiratory disease. Participants' past occupational exposures to diesel exhaust particles were evaluated to compare exposure rates between cases and controls.

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Cross-sectional studies

A cross-sectional study (also sometimes called a "cross-sectional survey") serves as an observational tool, where researchers capture data from a cohort of participants at a singular point. This approach provides a 'snapshot'— a brief glimpse into the characteristics or outcomes prevalent within a designated population at that precise point in time. The primary aim here is not to track changes or developments over an extended period but to assess and quantify the current situation regarding specific variables or conditions. Such a methodology is instrumental in identifying patterns or correlations among various factors within the population, providing a basis for further, more detailed investigation.

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Systematic reviews

A systematic review is a comprehensive approach designed to identify, evaluate, and synthesise all available evidence relevant to a specific research question. In essence, it collects all possible studies related to a given topic and design, and reviews and analyses their results.

The process involves a highly sensitive search strategy to ensure that as much pertinent information as possible is gathered. Once collected, this evidence is often critically appraised to assess its quality and relevance, ensuring that conclusions drawn are based on robust data. Systematic reviews often involve defining inclusion and exclusion criteria, which help to focus the analysis on the most relevant studies, ultimately synthesising the findings into a coherent narrative or statistical synthesis. Some systematic reviews will include a meta-analysis.

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Systematic review protocols

TODO

Meta analyses of Observational Studies

TODO

Randomised Trials

A randomised controlled trial (RCT) is a trial in which participants are randomly assigned to one of two or more groups: the experimental group or groups receive the intervention or interventions being tested; the comparison group (control group) receive usual care or no treatment or a placebo. The groups are then followed up to see if there are any differences between the results. This helps in assessing the effectiveness of the intervention.

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Randomised Trial Protocols

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Qualitative research

Research that aims to gather and analyse non-numerical (descriptive) data in order to gain an understanding of individuals' social reality, including understanding their attitudes, beliefs, and motivation. This type of research typically involves in-depth interviews, focus groups, or field observations in order to collect data that is rich in detail and context. Qualitative research is often used to explore complex phenomena or to gain insight into people's experiences and perspectives on a particular topic. It is particularly useful when researchers want to understand the meaning that people attach to their experiences or when they want to uncover the underlying reasons for people's behavior. Qualitative methods include ethnography, grounded theory, discourse analysis, and interpretative phenomenological analysis.

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Case Reports

TODO

Diagnostic Test Accuracy Studies

Diagnostic accuracy studies focus on estimating the ability of the test(s) to correctly identify subjects with a predefined target condition, or the condition of interest (sensitivity) as well as to clearly identify those without the condition (specificity).

Prediction Models

Prediction model research is used to test the accurarcy of a model or test in estimating an outcome value or risk. Most models estimate the probability of the presence of a particular health condition (diagnostic) or whether a particular outcome will occur in the future (prognostic). Prediction models are used to support clinical decision making, such as whether to refer patients for further testing, monitor disease deterioration or treatment effects, or initiate treatment or lifestyle changes. Examples of well known prediction models include EuroSCORE II for cardiac surgery, the Gail model for breast cancer, the Framingham risk score for cardiovascular disease, IMPACT for traumatic brain injury, and FRAX for osteoporotic and hip fractures.

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Animal Research

TODO

Quality Improvement in Healthcare

Quality improvement research is about finding out how to improve and make changes in the most effective way. It is about systematically and rigourously exploring "what works" to improve quality in healthcare and the best ways to measure and disseminate this to ensure positive change. Most quality improvement effectiveness research is conducted in hospital settings, is focused on multiple quality improvement interventions, and uses process measures as outcomes. There is a great deal of variation in the research designs used to examine quality improvement effectiveness.

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Economic Evaluations in Healthcare

TODO

Meta Analyses

A meta-analysis is a statistical technique that amalgamates data from multiple studies to yield a single estimate of the effect size. This approach enhances precision and offers a more comprehensive understanding by integrating quantitative findings. Central to a meta-analysis is the evaluation of heterogeneity, which examines variations in study outcomes to ensure that differences in populations, interventions, or methodologies do not skew results. Techniques such as meta-regression or subgroup analysis are frequently employed to explore how various factors might influence the outcomes. This method is particularly effective when aiming to quantify the effect size, odds ratio, or risk ratio, providing a clearer numerical estimate that can significantly inform clinical or policy decisions.

How Meta-analyses and Systematic Reviews Work Together

Systematic reviews and meta-analyses function together, each complementing the other to provide a more robust understanding of research evidence. A systematic review meticulously gathers and evaluates all pertinent studies, establishing a solid foundation of qualitative and quantitative data. Within this framework, if the collected data exhibit sufficient homogeneity, a meta-analysis can be performed. This statistical synthesis allows for the integration of quantitative results from individual studies, producing a unified estimate of effect size. Techniques such as meta-regression or subgroup analysis may further refine these findings, elucidating how different variables impact the overall outcome. By combining these methodologies, researchers can achieve both a comprehensive narrative synthesis and a precise quantitative measure, enhancing the reliability and applicability of their conclusions. This integrated approach ensures that the findings are not only well-rounded but also statistically robust, providing greater confidence in the evidence base.

Why Don't All Systematic Reviews Use a Meta-Analysis?

Systematic reviews do not always have meta-analyses, due to variations in the data. For a meta-analysis to be viable, the data from different studies must be sufficiently similar, or homogeneous, in terms of design, population, and interventions. When the data shows significant heterogeneity, meaning there are considerable differences among the studies, combining them could lead to skewed or misleading conclusions. Furthermore, the quality of the included studies is critical; if the studies are of low methodological quality, merging their results could obscure true effects rather than explain them.

Protocol

A plan or set of steps that defines how something will be done. Before carrying out a research study, for example, the research protocol sets out what question is to be answered and how information will be collected and analysed.

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