What is new?
Key findings- •
Clinicians' prognostic accuracy on patient clinical outcomes at discharge decreases when patients' clinical conditions are not clearly defined.
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Overall, clinicians' estimates substantially contribute to prediction accuracy. Furthermore, the Blaylock Risk Assessment Screening Score Index also had a moderate predictive performance.
What this adds to what was known?- •
Although older hospitalized patients are a substantial proportion of the clinical population, few studies have taken into account their clinical predictions, and to our knowledge, there are only few studies that have assessed clinical prediction in such a population for more than one outcome.
What is the implication and what should change now?- •
Caution should be used in making prognostic predictions for older patients, especially when the patients' clinical picture is uncertain and the illness course is not linear. However, it is precisely under these conditions that clinicians' prognostic skills are essential to guide treatment options.
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Careful consideration of specific clinical information available at admission can help to improve the accuracy of clinical outcomes prediction.
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The use of multiple statistical indices and predictive models underlines the importance of delivering more good quality prognostic research, to understand which clinical indices are of prognostic value.
In clinical practice, prognostics are commonly overshadowed by diagnostics and therapeutics [1], [2]. Physicians are often poorly prepared and feel uncomfortable in making predictions, especially when managing chronic diseases [3], [4]. Nonetheless, what patients and family members most expect from doctors is often accurate prediction on the course and outcome of the disease. Correct estimation of prognosis is particularly important both for clinical decision making and for an appropriate choice of the best treatment options [5], [6], [7]. Furthermore, prognostic research entails the “big business” of predicting the future in health care [8], taking into account a combination of multiple prognostic factors by making care more effective thanks to more informed clinical decision making [9], [10], [11].
Several studies have focused on the accuracy of physicians' prognostics for specific clinical populations, such as cancer patients [12], [13], [14], [15] and intensive care unit patients [16], [17], revealing a substantial degree of inaccuracy in predicting patients' actual outcomes. Although older patients are currently increasing in all high-income countries and therefore represent a substantial proportion of clinical populations treated in hospital settings, only a few studies have taken into account their clinical predictions [18], [19]. The prompt and accurate prognostication of patients who require long-term care could improve measured efficiency and the effectiveness of hospital care. This in turn could lead to fewer unnecessary hospital stays or inappropriate early discharge as well as speeding transfer to nursing homes or to long-term care facilities [20], [21], [22]. This would improve resource planning and contribute to make patients' expectations more realistic concerning their length of stay (LoS) [23], [24], [25], [26] and destination at discharge (DD) [27], [28], [29].
To investigate prediction accuracy of hospitalization outcomes for older patients, we examined predictions gathered from the Perdove-Anziani study cohort [30]. This longitudinal prospective study comprehensively evaluated older patients admitted to 10 geriatric units in northern Italy. Our first aim was to evaluate how accurately and under what conditions doctors can predict clinical outcomes for older patients. In particular, we focused on the following outcomes: (1) the patients' global functioning level at discharge, (2) overall LoS, and (3) their DD. Second, we devised predictive models for each outcome, combining the clinicians' estimates and other objective factors, such as the facility where patients were hospitalized and clinical assessment instruments administered at admission, to address future predictions.