From the Guidelines
Prognostic research is crucial in predicting health outcomes in patients with a specific condition or disease, and it involves studying factors that influence future health states to develop models that can forecast patient outcomes such as survival, disease progression, or quality of life. Prognostic research typically follows four key phases: identifying prognostic factors, developing prognostic models, validating these models in different populations, and assessing their impact on clinical decision-making and patient outcomes. According to a study by the European Association for Palliative Care 1, prognostication in advanced cancer has special importance, and accurate prediction of survival is necessary for clinical, organizational, and ethical reasons.
Key Phases of Prognostic Research
- Identifying prognostic factors: This phase involves collecting data on potential predictors including demographic characteristics, clinical measurements, biomarkers, genetic factors, and lifestyle variables.
- Developing prognostic models: Researchers analyze the relationships between potential predictors and outcomes using statistical methods to develop models that can forecast patient outcomes.
- Validating prognostic models: These models are validated in different populations to ensure their accuracy and reliability.
- Assessing impact on clinical decision-making and patient outcomes: The resulting prognostic models help clinicians estimate individual patient risks, guide treatment decisions, set realistic expectations, and inform resource allocation.
Importance of Prognostic Research
Prognostic research enables personalized medicine approaches, and good prognostic research requires adequate sample sizes, appropriate statistical methods, consideration of competing risks, and transparent reporting of methodology. The field continues to evolve with advances in artificial intelligence and machine learning improving prediction accuracy, as seen in studies such as the one published in the Journal of Clinical Oncology 1.
Applications of Prognostic Research
Prognostic tools like the Framingham Risk Score for cardiovascular disease or cancer staging systems enable clinicians to estimate individual patient risks and guide treatment decisions. By prioritizing prognostic research, clinicians can provide better care and improve patient outcomes, which is essential in real-life clinical medicine.
From the Research
Definition and Objectives of Prognostic Research
Prognostic research aims to describe the natural history and clinical course of health conditions, investigate variables associated with health outcomes, estimate an individual's probability of developing different outcomes, and investigate the clinical application of prediction models 2. The main objectives of prognostic studies are:
- Description: describing the natural history and clinical course of health conditions
- Association: investigating variables associated with health outcomes
- Prediction: estimating an individual's probability of developing different outcomes
- Causation: investigating the effect of prediction and decision rules on outcomes
Methodological Issues in Prognostic Research
Prognostic research has been poorly conducted and interpreted, indicating that conceptual areas are often misunderstood 2. Systematic reviews of prognostic factors have variable approaches to key methodological aspects, including study identification, selection, quality assessment, analysis, and reporting of findings 3. To address these issues, initiatives such as the Prognosis Research Strategy (PROGRESS) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Statement have been developed 2.
Statistical Methods in Prognostic Research
Statistical methods are used to assess the prognostic value of risk prediction rules in clinical research 4. These methods include:
- Discrimination: assessing the ability of a risk score to distinguish between individuals with and without an outcome (e.g., Harrell's C-index)
- Calibration: assessing the agreement between predicted and observed outcomes (e.g., Hosmer-May test)
- Risk reclassification: assessing the ability of a risk score to reclassify individuals into different risk categories (e.g., IDI, an index of risk reclassification)
Clinical Prediction Models
Clinical prediction models play an important role in contemporary clinical care, informing healthcare professionals and patients about outcome risks 5. Prognostic prediction models aim to calculate the risk of particular health states occurring in the future, while diagnostic prediction models aim to calculate an individual's risk that a disease is already present. Personalized prognostic models can be used to deliver supportive, palliative, and survivorship care services in the context of radiation therapy 6.