Biomarker testing in MCI patients—deciding who to test

Biomarkers such as amyloid beta1-42 (Abeta) and phosphorylated tau (p-tau) in cerebrospinal fluid (CSF) provide evidence on the neuropathological process underlying a patient’s cognitive decline [1]. Determining the underlying cause of cognitive complaints is particularly useful in the pre-dementia stage of mild cognitive impairment (MCI), as it provides important prognostic information [2]. Appropriate use criteria for the use of CSF biomarkers have been published, aiming to guide clinicians in the use of these biomarkers. In these criteria, longstanding and unexplained MCI is considered an indication for additional biomarker testing [3, 4]. The clinical practice guidelines of the American Association of Neurology (AAN) for MCI are more reluctant and recommend against the use of biomarkers in clinical practice as it is currently unclear how to value additional diagnostic testing in pre-dementia stages [5]. In line with this practice guideline, clinicians tend to implicitly steer against biomarker testing in MCI patients [6], even when multiple studies have shown the prognostic value of CSF biomarkers in MCI on a group level [7, 8]. We think that this suboptimal use of biomarkers in the clinic might be due to the lack of practical cost-efficient tools.

In a former study, we constructed personalized prognostic models that enable estimation of prognosis in terms of dementia conversion for an individual MCI patient, based on available biomarkers [9, 10]. We showed that the use of CSF biomarkers improves prognostic performance over the use of demographic information and magnetic resonance imaging (MRI) information. Nonetheless, biomarker testing is unlikely to contribute to a more accurate prognosis in every MCI patient [11‐13]. Here, we took as a starting point the notion that these same models could have additional value as a decision support tool, to aid clinicians in selecting patients for additional CSF biomarker testing.

We aimed to derive an algorithm to select MCI patients for CSF testing and to provide an estimate of the optimal proportion of patients to undergo CSF biomarker testing.

Table 1 presents the patient characteristics. Mean age of the MCI patients was 66 ± 8 years, 164 (41%) were female, and mean MMSE score was 27 ± 2 points. Overall, 189 (47%) patients progressed to dementia during 3 ± 2 years of follow-up.

In Fig. 1, the stepwise approach from demographic information only to additional CSF testing is shown. This figure shows the prognostic discrimination and prognostic accuracy of the stepwise model in comparison with demographic information only (Harrell’s C = 0.60, 3-year Brier score = 0.198) and demographics with additional CSF model when CSF results were included from all patients (Harrell’s C = 0.70, 3-year Brier score = 0.186). The discriminative performance of the stepwise model started to increase if 10% of the patients surrounding the median received CSF testing. The discriminative performance of the stepwise model gradually further increased, until it performed similarly to the CSF model (Fig. 1a) when 50% of the patients underwent CSF testing (Harrell’s C = 0.67). Brier scores showed a similar pattern and were comparable with the CSF models if also 50% of the patients received CSF (3-year Brier score = 0.190, Fig. 1b).

Figure 2 shows the stepwise approach from demographic and MRI information (Harrell’s C = 0.61, 3-year Brier score = 0.195) to additional CSF testing (Harrell’s C = 0.70, 3-year Brier score = 0.187). The stepwise model again started to increase if 10% of the patients received CSF testing and performed similarly to the CSF in all patients model (Fig. 2a) when 50% of the patients received CSF testing (Harrell’s C = 0.67). Brier scores showed a similar, although more wiggly, pattern and was comparable with the full CSF model if also 50% of the patients received CSF (3-year Brier score = 0.190, Fig. 2b). Table 2 shows the characteristics of patients that were and were not selected based on demographic and/or MRI information.

Subsequently, we evaluated the identified probability thresholds in the BioFINDER study (supplemental Table 1). Patient characteristics of the BioFINDER study are reported in supplemental Table 2. Applying the identified probability thresholds by the stepwise approach in the BioFINDER study would select 51% for CSF biomarker testing based on demographic information. Based on demographic and MRI information, the algorithm would select 48% of patients for additional CSF testing. CSF testing only in this proportion of patients yielded a better performance in comparison with CSF testing in none of the patients and a similar performance in comparison with CSF testing in all MCI patients (supplemental Table 3).

To illustrate the practical implementation of our algorithm for additional CSF biomarker testing, we present two clinical cases. For patient A, based on age (70 years), sex (female), and MMSE score (28), the 3-year progression probability was estimated to be 49.7%. This probability falls within the identified probability of the 50% of patients surrounding the median, and therefore additional CSF testing would be recommended. Adding CSF information (Abeta = 1188, p-tau = 47) resulted in a far lower progression probability of 17.8%.

For patient B, both demographic and imaging information were available. Based on age (54 years), sex (male), MMSE (29), and HCV (sum; 7 cm³), the 3-year progression probability was estimated to be 14.0%. This probability falls outside the identified probabilities of the 50% of patients surrounding the median based on demographic information and MRI. As the progression probability was already low, the algorithm does not recommend to add CSF testing. The progression probability of the ATN model (additional CSF testing; Abeta = 1349, p-tau = 44) for this patient was 9.2% and showed that CSF indeed did not meaningfully alter the estimated prognosis of this patient.

We developed an algorithm to identify those MCI patients most likely to benefit from additional biomarker testing. We showed that CSF biomarker testing adds prognostic value to clinical information in half of the MCI patients. The findings were replicated in an independent cohort. As such, we achieved a CSF saving recommendation without reducing prognostic accuracy.

In the decision to perform additional diagnostic testing, it is important to specify to what end a diagnostic test is performed, e.g., to identify or exclude Alzheimer’s disease (AD) pathology, predict clinical progression, change disease management, and/or improve well-being. The BIOMARKAPD project, a multidisciplinary working group, ranked these clinical questions on importance, and showed that CSF biomarkers are particularly useful to identify AD pathology and to predict progression to AD dementia in MCI patients [34]. Their recommendations are similar to those of the appropriate use criteria for CSF [4]. Both advise on CSF testing in all MCI patients. However, these recommendations are based on studies that investigated the additional value of CSF in terms of diagnostic or prognostic accuracy on a group level. In such studies, CSF is tested in all (MCI) patients and provides no information on the usefulness in specific patients. Moreover, the appropriate use criteria fairly state that a comprehensive clinical evaluation should precede the use of CSF biomarkers [4]. Clinicians should then determine, based on the available information, in which patients’ CSF biomarkers contribute to the diagnosis and clinical decision making. Such statements in the appropriate use criteria, however, are hard to operationalize for clinicians, especially in pre-dementia stages.

The current study provides clinicians with an easy-to-use algorithm that uses readily available information (i.e., age, sex, MMSE, and hippocampal volume if available) to identify MCI patients for CSF biomarker measurement. We took as a starting point progression probabilities based on basic clinical information only. By identifying the range of progression probabilities close to the progression prevalence in the population, where CSF is likely to add prognostic value, we allow the clinician to make an informed decision on performing biomarker testing. The clinician could also use this information to inform the patient before embarking on biomarker testing and manage expectations about potential outcomes. The communication of considerations to perform or not perform a diagnostic test was given high priority in a recent Delphi consensus study among clinicians, patients, and caregivers [35]. The BIOMAKAPD workgroup also acknowledges the importance of these considerations; they recommend that “in the case of positive biomarkers a personal follow-up plan should be offered and appropriate support should be initiated in the case of symptom progression”. And “in the case of negative AD biomarkers, an intensive follow-up plan may not be necessary”. Although this mentions implications for both possible outcomes, it is still in general terms and does not take available clinical information into account.

In the search for practical guidelines on which patient to test, several previous studies developed prediction models for amyloid positivity. Although these studies differ in their methodological details, they all focus on only one of the pathological hallmarks of Alzheimer’s disease as tauopathy and neurodegeneration are not considered [36, 37]. Moreover, most of these studies compare patients with AD dementia with controls and cannot be generalized to the MCI population. Finally, these algorithms identify individuals most likely to benefit from additional testing to identify amyloid positivity—most relevant in a trial setting, while in clinical settings, the clinical outcome, i.e., progression to (any type of) dementia is more relevant. One previous study used a computer algorithm to select patients in whom CSF testing was likely to contribute to a more accurate differential diagnosis for different types of dementia [38]. In this study, CSF testing was recommended in 26% of the cases. However, MCI patients were not included in this study. In the current study, we extended on the available literature with a keen eye for the needs in clinical practice by providing an algorithm to select MCI patients in whom CSF testing is most likely to contribute to a more accurate prognosis.

One of the strengths of the current study is that our algorithms make use of validated prognostic models to estimate the prognosis of each patient using available clinical information (patient characteristics and/or hippocampal atrophy). Moreover, we used measures that are easily available to the clinician, i.e., patient characteristics, the widely used MMSE score, and hippocampal volume. Although we described this stepwise approach for the decision to perform CSF testing in MCI patients, our approach has general applicability to investigate a stepwise approach from any two prognostic models. The novelty in our study is that we used a data-driven approach to define the proportion of patients that would benefit from additional biomarker testing, i.e., the performance of the stepwise approach should be significantly better than the clinical model and similar to the full (demographics, MRI and CSF) model. Similar approaches have been proposed for the classification of cancer samples by means of high-dimensional genomic markers [39] With our full model we have a measure for amyloid (A), tauopahty (T), and neurodegeneration (N) and thus align with the ATN criteria reported by the NIA-AA [1]. Lastly, we validated our stepwise approach in an independent cohort. The success of this validation may have resulted from the fact that BioFINDER patients had a similar risk profile compared to the MCI patients from Amsterdam, as similar diagnostic guidelines for MCI were used in both cohorts. The usefulness of this stepwise approach in a population with a different composition of risk profiles should be a topic for further research.

In conclusion, we showed that by performing CSF testing in 50% of the MCI patients the same prognostic accuracy is reached compared to testing all patients. Our algorithm uses prognostic models without CSF data to identify those patients most likely to benefit from CSF testing. This has important implications with respect to cost-efficient use of CSF testing. Furthermore, this approach also aids clinicians to set appropriate expectations before diagnostic testing.