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Age and outcome after aneurysmal subarachnoid haemorrhage

In 1941, Richardson and Hyland wrote that the prognosis of patients with ruptured aneurysms was worse with increasing age and if they had preexisting hypertension. Loss of consciousness at ictus, prolonged coma and focal neurological deficits also portended worse outcome. Prognostic models derived three quarters of a century later confirm these findings, albiet more rigorously; Jaja and colleagues developed prediction models for outcome after aneurysmal subarachnoid haemorrhage (SAH) and found few additional variables improved outcome prediction beyond admission neurological grade, age and preexisting hypertension. The relationship between patient age, clinical grade on admission and outcome after aneurysmal SAH is explored in detail by Ikawa, et al. The new finding is the relationship between age and poor outcome is not linear for patients admitted in World Federation of Neurological Surgeons (WFNS) grades 1–3, who are termed goodgrade patients. There was a steady increase with age in poor outcome up to an inflection point around 70 years old, after which the slope of this correlation increased substantially. On the other hand, poorgrade patients (WFNS grades 4 and 5) exhibited a linear increase in poor outcome as age increased. The authors suggest that their graphs of these relationships may be useful for clinicians estimating prognosis and for educating afflicted patients and their families. These are certainly common uses of such data, as in fully developed prognostic prediction models. 4 It should be obvious that treatment decisions should not be made on this data, the CIs are so wide that any predictions remain very uncertain. A unique aspect of this study is the use of machine learning, which aided in demonstrating the complex WFNS–age– outcome relationship. Conventional logistic regression data in the supplementary tables seem to match this although agreeably it is much more easily seen graphically. The results make one think about other aspects of the data, such as that the relationship of another prognostic factor, the Fisher grade, to outcome is not necessarily linear either. I would ignore the authors’ discussion about differences between clipping and coiling since the CIs are wide and the patients were not randomised to clipping or coiling. There are other potentially valuable uses for this work. Prognostic models can aid in selecting patients for and increasing the power of randomised clinical trials. They may guide application of specific treatments to subsets of patients. SAH is an uncommon disease and it is a struggle to adequately power clinical trials. While many such trials stratify patients at randomisation based on prognostic factors like WFNS grade, this does not affect sample size. However, adjusting for known prognostic factors for outcome during the analysis phase can. This study’s results, if confirmed, would have to be considered in any such adjustment. Limitations of this work should be mentioned. The patients are highly selected from a clinical registry and cases were included ‘in principle’. There is no way of knowing about any missed cases. The cases are not from a random sample of hospitals in Japan. Patients were further selected by requiring complete data and having had aneurysm repair. Less than half the registry SAH patients ended up being included. The primary outcome (modified Rankin scale) was determined at discharge. Patients improve between discharge and, for example, 3 months after SAH, so if patients of different ages and WFNS grades recover at different rates, then the results might change. Seventytwo per cent of cases underwent aneurysm clipping, whereas in many other countries, this would be the per cent for endovascular repair. Steps in development of prediction models include development, evaluation/ validation and application/impact assessment. Ikawa and colleagues report the development phase and correctly note that the findings need validation. Ideally, this would include large numbers of patients with SAH from diverse settings. Life expectancy in Japan is uniquely long, so the curves generated here may be specific to Japan. Getting data sets of thousands of cases for validation is not easy. The authors graciously agree to share their data on request. Some groups will not share their data, although others are willing to.