Observation and description of the fundamental characteristics of the problem

Before we begin designing interventions for a given illness, we should understand how that illness unfolds naturally. For instance, oncologists characterise cancers by culturing cells, identifying associated genes and recording patients’ symptoms. Psychiatrists describe mental illnesses such as first-episode psychosis or the progression of manic episodes with similar attention to detail. Establishing a clear baseline allows researchers to define thresholds for treatment effectiveness. However, there has been far less phenomenological characterisation of STBs. STBs are relatively low-incidence behaviours and their onset is difficult to predict and unethical to promote. Therefore, most studies rely on retrospective patient report, which is subject to recall bias. Furthermore, sampling frequency is often low. Fewer than 1% of studies sample patients more than once a month, which results in a failure to capture the dynamic nature of STBs.^{Reference Franklin, Ribeiro, Fox, Bentley, Kleiman and Huang1}

With the recent advent of smartphones and wearable devices, higher-frequency and higher-fidelity sampling methods can facilitate major advances in our understanding of STBs. We advocate the use of methods that better capture the phenomenon of interest. Real-time monitoring techniques reduce recall biases and help us better understand how STBs change dynamically over time. Importantly, researchers have been able to demonstrate that even the most high-risk psychiatric in-patients are willing and able to engage in wearing biosensors and answering smartphone-based questions multiple times per day.^{Reference Kleiman, Turner, Fedor, Beale, Picard and Huffman2} Once we develop an understanding of the phenomenology of STBs, we also may better understand how treatments affect patients. Careful, real-time, dynamic modelling of the changes in STBs and related symptoms during treatment can help advance understanding of how and for whom our treatments (e.g. medication, electroconvulsive therapy/transcranial magnetic stimulation, psychological interventions) work best.

Increased clinical utility of risk assessments

One of our most pressing clinical needs is the development of a reliable and clinically actionable risk assessment tool for STBs. A recent meta-analysis revealed that the predictive power of individual risk factors for STBs is low and has not increased over the past 50 years.^{Reference Franklin, Ribeiro, Fox, Bentley, Kleiman and Huang1} This meta-analysis also showed that research has been re-reporting on these same individual risk factors for STBs (e.g. mental disorders, stressful life events) for decades. Recent studies using machine learning applied to patient electronic health records to build suicide prediction models have provided a long-needed step forward, although here too additional progress is needed.

Current approaches in neurology provide some guidance on potential next steps for suicide research. Using clinical outcomes data, neurologists have derived a widely used risk assessment for ischaemic stroke in patients with atrial fibrillation. To quantify this risk, researchers created the CHADS₂ calculator, an easy-to-administer scoring system that categorises patients into meaningful risk groups. The lowest score of 0 confers a stroke risk of 1.9 stroke events per 100 patient-years, whereas the highest score of 6 carries a meaningfully higher risk of 18.2.^{Reference Gage, Waterman, Shannon, Boechler, Rich and Radford3} This assessment tool assists clinicians in assessing the costs and benefits of prescribing an anticoagulant medication.

What can we learn from such advances? To the extent possible, risk assessments should include items that can be objectively verified. The CHADS₂ stroke risk score includes verifiable items such as age, gender and other medical diagnoses. Many suicide risk factors are based on more subjective self-reports rather than objective ones (e.g. normal blood sugar levels) and therefore are less reliable. Suicide researchers recently have showed that objective behavioural tests such as the Implicit Association Test can effectively predict future suicidal behaviour. Incorporating more objective approaches can bring psychiatry more in line with other branches of medicine. Last, it is important to acknowledge that, despite the low positive predictive value (PPV) of the current suicide prediction models derived from large-scale data sources, they can still provide valuable information for clinical decision-making.^{Reference Belsher, Smolenski, Pruitt, Bush, Beech and Workman4,Reference Simon, Shortreed and Coley5}

Using subtyping to reduce complexity

Until the 20th century, cancer was considered a diagnosis with few meaningful categories and even fewer treatment options. With advances in science, such as the advent of gene sequencing, oncologists began to discern more subtypes of cancers. With clearer categorisation, researchers now can better target the right combination of chemotherapy, radiation and/or medications for each subtype.

Like the process of cancer growth, the processes leading to STBs are similarly complex and heterogeneous. Suicide can be intricately planned, result from an act of impulse or fall somewhere in between. Consequently, the predictors of STBs for a person with schizophrenia likely differ significantly from those for a person with no discernible mental disorder. Instead of conceptualising STBs as a monolithic syndrome, we advocate studying STBs on the basis of meaningful clinical subcategories beyond clinical diagnoses using multiple data streams. By doing so, researchers can better design tailored interventions for each subpopulation. Recent research has shown that people experiencing suicidal ideation can be reliably classified into one of five different subtypes.^{Reference Kleiman, Turner, Fedor, Beale, Picard and Huffman2} This is just one example of how subtyping of people experiencing STBs can be used to reduce the complexity and heterogeneity of those suffering from these conditions, perhaps providing much-needed traction in understanding, predicting and preventing these outcomes.