Disentangling Multiple Sclerosis and depression: an adjusted depression screening score for patient-centered care

Abstract

Screening for depression can be challenging in Multiple Sclerosis (MS) patients due to the overlap of depressive symptoms with other symptoms, such as fatigue, cognitive impairment and functional impairment, for MS patients. The aim of this study was to understand these overlapping symptoms and subsequently develop an adjusted depression screening tool for better clinical assessment of depressive symptoms in MS patients. We evaluated 3,507 MS patients with a self-reported depression screening (PHQ-9) score using a multiple indicator multiple cause modeling approach. Our models showed significant differential item functioning effects denoting significant overlap of depressive symptoms with all MS symptoms under study and good model fit. The magnitude of the overlap was especially large for fatigue. Adjusted depression screening scales were formed based on factor scores and loadings that will allow clinicians to understand the depressive symptoms separate from other symptoms for MS patients for improved patient care.

Appendix: More details about adjusted depression screening scale algorithms

Factor scores

Factor scores include adjustment for the effect of both CFA as in Fig. 1 Panel C, since the nine PHQ-9 items load unequally on depression, and the adjustment of overlapping symptoms via covariates and DIF paths from our E = 0 model in Fig. 1 Panel F. Using MPlus, we calculate factor scores within our sample using the maximum a posteriori method (Muthén and Muthén, 2012), which for continuous outcomes is the widely used regression method: mean = 0, variance = 0.56, median = − 0.21, range = (−0.84, 2.47), 1st quartile = −0.62, and 3rd quartile = 0.40. These factor scores can then be linearly transformed to fit any potentially useful range (i.e. 0–27, 0–100, etc.)

In our application, and for immediate clinical use, a probability integral transformation (Casella and Berger, 2002) is used to transform these factors scores into the PHQ-9 scores within this population to maintain the interpretation and thresholds of the PHQ-9. This is a one-to-one transformation, so the distribution of PHQ-9 scores in this population will remain the same, just that certain individuals will be matched to different PHQ-9 scores. This transformation is built on continuous cumulative distribution functions and thus there are no special steps required for tied adjusted scores between two or more individuals (unlikely for factor scores). To perform the transformation:

• 1. Use a kernel density estimator to estimate the density of the PHQ-9 scores in this population.

• 2. Obtain the cumulative distribution function by numerical integration.

• 3. Numerically invert the cumulative distribution function for the PHQ-9.

• 4. Repeat steps (1) and (2) for the factor scores.

• 5. Transform the factor scores of each individual into a PHQ-9 score for this population by transforming the cumulative distribution function for the factor scores using the inverse of the cumulative distribution function for the PHQ-9.

The resulting factor scores will be distributed the same as the PHQ-9 within this population. For ease of use in a clinical setting, the recommendation is to round these transformed scores off to the nearest whole number.

Note, within EHR-based database, every time a new or series of new PHQ-9 scores is entered, we can compute the factor scores. Then, either all subjects can be updated or we can keep prior records the same, and just use the functions to transform the new scores only. Using this approach, only 44 % of subjects maintain the same transformed adjusted score as the original PHQ-9 score, with 16 % of subjects scoring a transformed adjusted score of two points or more (with three subjects up to 6 points) different than the original PHQ-9 score. Further, 74 subjects had a PHQ-9 score ≥10 and now have a transformed adjusted score <10, while 65 subjects had a PHQ-9 score <10 and now have a transformed adjusted score ≥10.

Item weights

Compared to this algorithm in “Item weights” based on factor loadings, the factor scores algorithm in “Factor scores” results in a more mathematically rigorous individualized score, taking into account item loadings, intercepts, correlation among observed variables, and a predictive equation. However, in the factor scores algorithm, we do not quantify how much each item contributes.

We can directly build a scale using the factor loadings as weights:

$$ \begin{gathered} {\text{Item i Weight}} = Factor{\text{ l}}oading \, i \hfill \\ {\text{MS-adjusted PHQ-9 score}} = \sum\limits_{i = 1}^{ 9} {Item \, i \, Weight \times Item \, i \, PHQ - 9{\text{ s}}core} \hfill \\ \end{gathered} $$

(1)

In this case we use the factor loadings in Table 4 for E = 0 as item weights and using (1), in our sample, the mean = 3.95 and standard deviation = 3.71. Similarly, we can use the probability integral transformation for this algorithm as described for the factor scores algorithm in “Factor scores”. In this case, there will be ties (same scores on MS-adjusted PHQ-9), but as explained above this will not be an issue with this transformation. The results here are a lot more conservative in change from the standard scoring, in that 64 % of subjects maintain the same score and only 2 % have greater than or equal to a two points or more difference from the original PHQ-9. As a result, this score may not be particularly useful in the transformed version, since it is a naïve approach of algorithm “Factor scores”, and may be more useful once more rigorous psychometric evaluation of this score is performed.

Downweighting overlapping symptoms

We only downweight the influence of items of the PHQ-9 in which depressive symptoms overlap with other symptoms for MS patients:

$$ {\text{Item i Weight}} = \frac{{E_{i} }}{{CFA_{i} }} $$

(2)

where E_i is the item i standardized factor loading for E = 0 and CFA_i is the item i standardized factor loading for the one factor CFA model (see Table 4).

We multiply a patient’s score on items for sleep problems by 0.79, fatigue by 0.52, poor concentration by 0.70 and psychomotor symptoms by 0.74. PHQ-9 items for appetite change, feelings of failure and self-harm maintain the same score. Items for anhedonia and feel depressed, have an almost negligible residual DIF effect and we multiply a patient’s score on these items by 1.01. We observe for our adjusted PHQ-9, a population mean = 5.66, standard deviation = 5.30, and median = 4.07 with a range = (0, 23.32) and 1st quartile = 1.31, and 3rd quartile = 8.53. As noted, an application of “Downweighting overlapping symptoms” is to subtract this score from the PHQ-9, as an estimate of the amount that items on the PHQ-9 overlap with other symptoms for MS patients.

Using the PHQ-2

The PHQ-2 comprises the first two items of the PHQ-9 and has been used as a depression screening tool or as a pre-screener for the PHQ-9 (Kroenke et al., 2003). Thus, using this shortened scale will bypass evaluating items for sleep problems, fatigue, poor concentration and psychomotor symptoms. A PHQ-2 score of three has been used as a threshold for depression for screening purposes (Kroenke et al., 2003). The PHQ-2 is highly correlated with the PHQ-9 in this MS study population (pearson ρ ≈ 0.87). Further, a receiver operating characteristic (ROC) analysis of PHQ-9 ≥ 10 versus PHQ-2 ≥ 3 in this study population, showed high specificity = 95.7 and a large positive predictive value (PPV) = 91.2, and area under the curve (AUC) = 0.852, though at the expense of the test sensitivity = 63.8. In general, the PHQ-2 has shown wide variability in sensitivity in previous validation studies and more research is needed to see if its diagnostic properties approach those of the PHQ-9 (Gilbody et al., 2007).