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February 26, 2019; 92 (9) Article

Value of witness observations in the differential diagnosis of transient loss of consciousness

Min Chen, Jenny Jamnadas-Khoda, Mark Broadhurst, Melanie Wall, Richard Grünewald, Stephen J.L. Howell, Matthias Koepp, Steve W. Parry, Sanjay M. Sisodiya, Matthew Walker, Dale Hesdorffer, Markus Reuber
First published January 25, 2019, DOI: https://doi.org/10.1212/WNL.0000000000007017
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Abstract

Objective This retrospective study explores to what extent additional information from event witnesses provided using the novel 31-item Paroxysmal Event Observer (PEO) Questionnaire improves the differentiation among epilepsy, syncope, and psychogenic nonepileptic seizures (PNES) achievable with information provided by patients alone.

Methods Patients with transient loss of consciousness caused by proven epilepsy (n = 86), syncope (n = 79), or PNES (n = 84) attending specialist neurology/syncope services in the United Kingdom and event observers provided Paroxysmal Event Profile (PEP), PEO, and personal information (PI) (e.g., sex, age, medical history) data. PEO data were subjected to exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA). PEO, PEP, and PI data were used separately and in combination to differentiate diagnoses by pairwise and multinomial logistic regressions. Predicted diagnoses were compared with gold standard medical diagnoses.

Results EFA/CFA identified a 4-factor structure of the PEO based on 26/31 questionnaire items with loadings ≥0.4. Observer-reported factors alone differentiated better between syncope and epilepsy than patient-reported factors (accuracy: 96% vs 85%, p = 0.0004). Observer-reported data improved accuracy over differentiation based on patient-reported data alone from 90% to 100% between syncope and epilepsy (p = 0.005), 76% to 83% between epilepsy and PNES (p = 0.006), and 93% to 95% between syncope and PNES (p = 0.098).

Conclusions Information from observers can make an important contribution to the differentiation of epilepsy from syncope or PNES but adds less to that of syncope from PNES.

Glossary

ANOVA=
analysis of variance;
CFI=
comparative fit index;
EFA=
exploratory factor analysis;
PEO=
Paroxysmal Event Observer;
PEP=
Paroxysmal Event Profile;
PI=
patient information;
PNES=
psychogenic nonepileptic seizures;
RMSEA=
root mean square error approximation;
TLI=
Tucker Lewis index;
TLOC=
transient loss of consciousness

The gold standard for the diagnosis of transient loss of consciousness (TLOC) is the simultaneous recording of clinical events and physiologic measures.1 However, this diagnostic standard can rarely be achieved in routine care, and most diagnoses are made on the basis of the patient's history. It is therefore perhaps not surprising that misdiagnosis rates of over 25% have been reported.2,–,4

We recently demonstrated that self-reportable TLOC-associated symptoms can make an important diagnostic contribution when data about possible symptoms are collected using the Paroxysmal Event Profile (PEP), an 86-item questionnaire asking patients how commonly they have experienced particular symptoms.5 Using laboratory-proven diagnoses of epilepsy, psychogenic nonepileptic seizures (PNES), and syncope as a standard, 66% of patients with epilepsy, 91% with syncope, and 78% with PNES were classified correctly when PEP data were combined with self-reported basic patient information (PI).

Although it is standard practice to ask TLOC witnesses about their observations, there is remarkably little research about the additional value of information such individuals can provide. One prospective study showed that observer information can contribute to the differentiation of epilepsy and PNES,6 although another demonstrated the limited predictive value of such data.7 Another study indicated that the failure to seek witness information is an important reason for misdiagnoses.4 However, witness reporting errors are common.8,–,11

This study investigates to what extent a 31-item profile of observer-reportable event manifestations improves the diagnostic differentiation achievable with information provided by patients.

Methods

PEP respondents

A total of 300 patients from 3 British medical centers, 100 in each group (epilepsy, syncope, PNES), completed an 86-item PEP capturing TLOC-associated symptoms and provided data for this retrospective study. In addition, PEP respondents (aged >16 years) provided basic clinical and demographic PI (self-reported data on patient sex, number of episodes of TLOC in last year, lifetime number of hospitalizations and admissions to intensive care for the treatment of TLOC, family history of TLOC). All diagnoses were confirmed by recordings of typical events with relevant tests (video-EEG, ambulatory ECG, tilt-table). Recruitment method and formulation of gold standard diagnoses have been described previously.5 The present study is based on the same dataset but only includes patients for whom additional paroxysmal event observer data were available.

Paroxysmal event observers

Patient participants in this study were asked to identify an observer of their events and to pass the Paroxysmal Event Observer (PEO) questionnaire and an information sheet to the witness.

PEO questionnaire

After asking how many events respondents have observed and how long they have known the patient, the PEO questionnaire, created by the authors and other experts, asks about 31 observable TLOC manifestations using a 5-point Likert scale (always to never).

Study procedure

PEP and PEO questionnaires were sent out by post with a free return envelope and information sheet stating that return of the completed questionnaire would be interpreted as consent.

Sample size

A sample of 100 patients per diagnostic group provides adequate power (>80%) to detect moderate differences between groups on any item or factor score (effect sizes ≥0.40). There are no standard power calculation methods for exploratory factor analysis (EFA), but at least 5–10 individuals for each item analyzed is commonly recommended; hence our sample is sufficiently large to accommodate factor analyses on the 31-item PEO.12

Statistical analysis

We examined patient clinical and demographic data and witness information using analysis of variance (ANOVA) for continuous and χ2 tests for categorical variables. We calculated mean feature manifestation scores of PEO items for each participant by diagnostic groups and made comparisons using ANOVA. We tabulated frequencies of reporting extremes (never or always responses) and carried out between-group comparisons using χ2 statistics. We also visualized the mean PEO item scores using heatmap2 in the gplot package for R.

We conducted EFA of the PEO items using geomin oblique rotation, retaining items with factor loadings ≥0.4. In a confirmatory factor analysis (CFA), we subjected the factor structure suggested by EFA to goodness of fit statistics, including root mean square error approximation (RMSEA), comparative fit index (CFI), and Tucker Lewis index (TLI). We predetermined RMSEA <0.06, CFI >0.90, and TLI >0.90 as standards for good model fitting.

We compared mean factor scores across diagnostic groups and tested differences using 1-way ANOVA. To assess the ability of all factors simultaneously to discriminate participants by diagnostic groups, we used pairwise and multinomial logistic regression analyses with all identified factor scores as continuous predictors. We also produced separate models using only one factor score at a time. In addition to separate models based entirely on the previously reported PEP factors (subjective symptoms) and additional PI, regressions were based on PEO data alone. We did not include age and age at onset in the models because syncope patients were predominantly recruited in a health care setting for older adults. We included all other clinical variables as categorical predictors.

We conducted all factor analyses in Mplus version 7.0. We performed logistic regression in SAS version 9.3 for Windows. We controlled the ANOVA on 31 items for multiple testing (i.e., significance was determined at 0.05/31 = 0.0016). Unless specified, we considered 2-sided p values ≤0.05 statistically significant.

Standard protocol approvals, registrations, and patient consent

We received ethical approval for this study from the Northern and Yorkshire Multi-Centre Research Ethics Committee.

Data availability statement

The individual respondent-level data on which our analyses are based are not provided because of space limitations, but anonymized data can be shared at the request of qualified investigators for purposes of replicating procedures once relevant ethics permission for the further analyses have been obtained.

Results

Respondents

Of 300 TLOC patients who returned PEPs, 249 included PEO questionnaires (86 with epilepsy, 84 with syncope, and 79 with PNES). The demographic and clinical characteristics of this subpopulation of patients are summarized in table 1. They were similar to those of the whole PEP patient group reported previously.5 The population on which PEO data were available did not differ on any of the available items of PI from the 51 PEP respondents who did not provide additional observer data (data not shown).

View this table:
Table 1

Descriptive information about patients and witnesses

Descriptive findings

Mean PEO item scores are shown in figure 1 (for further details, see table 2). Witnesses in the syncope group reported fewer event manifestations overall (mean score 2.3, where 1 is never and 5 is always) than those with epilepsy (mean score 2.8) or PNES (mean score 2.9, differences: PNES vs syncope p < 0.0001, epilepsy vs syncope p < 0.0001) (figure 1). Syncope was also associated with a smaller range of different manifestations as reflected by the greater percentage of never replies in the syncope group (59.4%) than in the epilepsy (32.5%) or PNES (31.2%) groups (differences: epilepsy vs syncope p < 0.0001, PNES vs syncope p < 0.0001). The PEO profiles for patients with PNES or epilepsy suggested similar levels of heterogeneity of observer-reportable event manifestations in these groups.

Figure 1
Figure 1 Range and frequency of witness-reported transient loss of consciousness symptoms in patients with epilepsy, syncope, and psychogenic nonepileptic seizures (PNES)

Graphic representation of respondents' mean answers to the 31 questions posed in the Paroxysmal Event Observer questionnaire illustrating the relative diagnostic value of individual items. Mean answers are indicated for each group by the light blue line. The shade of the background color also indicates the mean response (with darkest shades corresponding to “always” and the white to “never” replies).

View this table:
Table 2

Comparison of responses to individual Paroxysmal Event Observer items in the epilepsy, psychogenic nonepileptic seizures (PNES), and syncope groups

Responses to 24 out of 31 items differed among the 3 diagnostic groups in an ANOVA (p < 0.0016). Only 4 items (W2, W4, W6, and W7) did not differentiate among the 3 groups at the more liberal p < 0.05 level.

Latent factor analysis

EFA models with 1 to 5 factors were tested. Five factors provided the best model fit, but the 4-factor model was selected for its better interpretability and because it still had a good fit (RMSEA = 0.06 and CFI/TLI ≥0.90). Of the 31 items, 26 had factor loadings ≥0.4. Four loaded on 2, the remaining items on 1 latent factor. The 4-factor structure, with the selected 26 items, was tested by CFA. The fit indices of the CFA model were CFI = 0.93, TLI = 0.92, and REMSA = 0.079.

Based on our semantic interpretation, the 4 factors were named “unconsciousness,” “reduced self-control,” “excessive movement,” and “skin/face/recovery” (see table 3 for more details). Mean factor scores from the CFA analysis across the epilepsy, PNES, and syncope groups are shown in table 2 and figure 2. The factor “reduced self-control” differed among all 3 groups (p < 0.001). For “excessive movement” and “skin/face/recovery,” the syncope group differed from the other two (p < 0.001), whereas the differences between the epilepsy and PNES groups narrowly failed to achieve significance (p = 0.056 and 0.075, respectively). The factor “unconsciousness” differentiated epilepsy from syncope (p = 0.005).

View this table:
Table 3

Four latent factors: summary profile and sample questions (see figure 2 for a graphic representation of the differences in factor profiles among epilepsy, psychogenic nonepileptic seizures [PNES], and syncope)

Figure 2
Figure 2 Observer-reported transient loss of consciousness (TLOC) symptom profiles in epilepsy, syncope, and psychogenic nonepileptic seizures (PNES)

TLOC manifestation profiles based on 4 latent factors characterizing observer-reported experiences of the 3 common causes of TLOC.

Differential diagnostic value of latent factors

Pairwise logistic regression among each pair of the 3 possible clinical diagnoses using the 5 patient-reported factors and demographic/clinical PI was repeated on patients with available observer data (table 4), and similar rates of correct classification as published previously were observed.5 Observer-reported PEO factors differentiated syncope and epilepsy better than patient-reported PEP factors (accuracy: 96% vs 85%, C-index p = 0.0004). When the analysis of information provided by patients (PEP and PI) and observers (PEO) was combined, the accuracy of this distinction rose from 90% to 100% (C-index p = 0.005). In the differentiation of PNES and epilepsy, additional observer-reported factors improved prediction accuracy from 76% to 83% (C-index p = 0.006), in that of PNES and syncope from 93% to 95% (C-index p = 0.098).

View this table:
Table 4

Analysis of variance demonstrating the differentiating potential of individual factor scores among the 3 possible causes of transient loss of consciousness

Multinomial logistic regression analysis revealed a similar contribution of observer-reported factors (table 5). Using PEP and PI data only, 55 out of 84 (65%) epilepsy diagnoses, 60 out of 80 (75%) PNES diagnoses, and 69 out of 79 (87%) syncope diagnoses could be predicted correctly (nearly the same as previously reported using the full 300-participant dataset).5 With the addition of observer-reported factors extracted from the PEO questionnaire, these percentages rose to 80%, 79%, and 92%, respectively.

View this table:
Table 5

Binary logistic regression demonstrating differentiating potential of patient- or observer-derived factor scores or demographic/clinical patient information or combinations

Discussion

The interpretation of information provided by patients and witnesses is the most important aspect of the diagnostic process in TLOC.2,13 Several previous studies have demonstrated that brief questionnaires exploring patient-reportable TLOC-associated symptoms can distinguish between syncope and tonic clonic seizures,14,15 but the differentiation of epilepsy from PNES, the third common cause, is more difficult.5,16 Questions about specific single event manifestations (such as ictal injuries, seizures from sleep, or eye closure during seizures)17,18 do not differentiate well between epilepsy and PNES even though video-EEG observation of these features can help to distinguish between these causes of TLOC. However, in a previous study we demonstrated that comprehensive profiles of patient-reportable TLOC-associated symptoms (when combined with basic clinical and demographic PI) can contribute to the diagnosis. Such profiles, generated on the basis of the 86-item PEP, allowed us correctly to classify 65% of patients with epilepsy, 75% of those with PNES, and 87% of those with syncope.5 Although the art of history-taking is likely to be as old as medicine itself, previous research has neglected the question by how much the diagnostic categorization of TLOC patients can be improved when patient-reportable information is combined with additional information from witnesses.

When analyzed in isolation, more patients were correctly classified by the observer-derived PEO data than the clinical/demographic or TLOC symptom data provided by patients themselves. The observation that witness reports can be strongly predictive of objectively proven diagnoses concords with the findings of a study in which witness responses to a 12-item questionnaire about seizure manifestations were compared with video-EEG confirmed diagnoses of epilepsy or PNES. In this study, the witness questionnaire correctly classified patients with a sensitivity of 84.4% and a specificity of 84.2% (although the diagnostic approach was not tested in a separate validation sample).19 These facts emphasize the value of eyewitness accounts when available.4 They also provide justification for allocating consultation time to event witnesses in diagnostic encounters with patients with TLOC, even if this reduces the time available for patients to discuss subjective experiences.20

Questions to the PEO factor “reduced self-control” distinguished most clearly among all 3 groups, with features suggesting that low levels of TLOC-associated self-control are more frequently observed in those with epilepsy than those with PNES or syncope. Some single items provided interesting insights: responses suggested that, unlike syncope and PNES, epileptic seizures “never” looked like normal sleep. Previous studies have indicated that pale skin and limp collapse are typical of syncope rather than epilepsy14,21; the PEO responses suggest that these features also distinguish syncope from PNES. Only PNES observers typically replied that they could “never” do something to make the attack pass more quickly.

Witness information contributed most to the correct differentiation of epilepsy from syncope or PNES and less to the differentiation of syncope from PNES (which was highly accurate on the basis of patient-provided data in any case). However, when all available data were combined, the poorest differentiation was that between epilepsy and PNES. While witness information improved diagnostic accuracy, this study also demonstrates that additional data will be required to optimize this particular diagnostic differentiation. Previous studies suggest that questions probing domains such as psychopathology, personal and psychiatric history, trauma, coping, and other somatic symptoms may improve diagnostic categorization.6,22,–,26 Patients with chronic PNES and epilepsy also differ in personality profiles.27,28 What is more, the differentiation of epilepsy and PNES on the basis of the patient's history can be improved when doctors do not just focus their attention on what symptoms patients describe but also on how they talk about their TLOC experiences, or which aspects of these experiences they volunteer and highlight when they talk to a doctor.29,–,31

The findings of this study suggest that it should be feasible to develop a useful patient and witness questionnaire for initial TLOC diagnoses. Such diagnostic tools, inspired by the PEP and PEO questionnaires, could be used in emergency or general medical settings to help direct patients to the most appropriate specialist services and provide a numeric pretest probability of the most common causes of TLOC, thereby enhancing the diagnostic value of interictal investigations such as ECG, EEG, or brain imaging.

Our study has a number of limitations. We chose to approach patients with laboratory-proven diagnoses. We thought that best possible medical comparator diagnoses generated by alternative diagnostic approaches (such as the comparison of the findings of the tool under investigation with working diagnosis after a certain period of follow-up,14,21 the opinion of an experienced clinician,32 or the diagnosis recorded in medical registers33,34) would have been influenced too strongly by patients' and observers' responses to the questions included in the PEP and PEO, and may simply have confirmed clinicians' preconceptions about the diagnostic value of particular seizure manifestations. While the approach we have chosen allows us to be quite certain about actual medical diagnoses, it also means that participants had rather chronic disorders and experienced more events than most patients seen in diagnostic settings. The chronicity of the seizure disorders captured in this study is also likely to explain the fact that patients with epilepsy and PNES reported more hospital and intensive care unit admissions than those with syncope. Furthermore, observers of chronic disorders may have been able to provide more detailed and accurate information about the events than witnesses typically can when they have seen a first episode of TLOC. The fact that all participants (and most witnesses) were aware of their particular diagnosis may have shaped their perception and report of particular features. The fact that more observers in the syncope than the other 2 groups reported having based their responses on witnessing a single event (which may have been quite remote) could, at least partially, explain the greater phenomenologic homogeneity emerging from PEO responses about this cause of TLOC. This observation may also have resulted from our decision to allow unselected patients with TLOC-associated epilepsy or PNES to take part in this study, not just patients with particular seizure types. For instance, the largest previous study of a similar nature restricted participation in the epilepsy group to those with bilateral tonic clonic seizures.14 One important advantage of not having restricted recruitment to patients with particular types of seizures is that our findings should be more readily generalizable to nonselective diagnostic settings.

Another limitation of our findings is that only about 30% of the patients contacted about the study returned PEP data and that 17% of the PEP respondents did not provide additional PEO data. We did not observe any clinical or demographic differences between those initial respondents who provided additional PEO data and those who did not, but we cannot be certain how representative the 300 PEP respondents were of the larger patient pool contacted about the study or patients presenting to diagnostic settings. The generalizability of our findings may also be limited by the fact that there was an equal sex ratio in the 3 respondent groups although in clinical practice, there is a marked preponderance of females in patient groups with PNES that is not seen in those with syncope or epilepsy.

We are also unable to rule out that the reporting of witnesses' observations was influenced by them being aware of the patient's diagnosis or by the nature of their relationship with the patient (about which data were not captured). For instance, patients with PNES and their families may have been encouraged by doctors to consider whether events may have emotional triggers.

Despite these limitations, our study clearly demonstrates that a comprehensive profile of witness-observable TLOC manifestations can make an important contribution to differentiation among the 3 commonest causes of TLOC: epilepsy, syncope, and PNES. The best differentiation can be achieved if TLOC observer-derived information is combined with clinical/demographic information from patients as well as a comprehensive profile of subjective TLOC symptoms. The combination of these data will correctly classify 80% of patients with epilepsy, 79% of those with PNES, and 92% of those with syncope. While prospective validation of our findings is required, clinicians involved in the differential diagnosis of TLOC should make a determined effort to obtain information from event witnesses to reduce diagnostic errors. Data beyond TLOC manifestations are particularly important for the distinction of epilepsy from PNES.

Author contributions

M. Reuber: conceptualization of research project, development of Paroxysmal Event Profile (PEP) and Paroxysmal Event Observer (PEO) questionnaire, obtaining regulatory approval, identifying participants, confirming participants' diagnoses by chart review, data collection, statistical analysis, drafting of manuscript. M. Chen: statistical analysis, drafting of manuscript. J. Jamnadas-Khoda: obtaining regulatory approval, identifying participants, data collection, data entry, drafting of manuscript. M. Broadhurst: conceptualization of research project, development of PEP and PEO questionnaire, obtaining regulatory approval, drafting of manuscript. M. Wall: analytic strategy development, statistical analysis, drafting of manuscript. R.A. Grünewald: development of PEP questionnaire, identifying participants, confirming participants' diagnoses by chart review, drafting of manuscript. S.J. Howell: development of PEP questionnaire, identifying participants, confirming participants' diagnoses by chart review, drafting of manuscript. M. Koepp: identifying participants, confirming participants' diagnoses by chart review, drafting of manuscript. S. Parry: development of PEP and PEO questionnaire, obtaining regulatory approval, identifying participants, confirming participants' diagnoses by chart review, data collection, drafting of manuscript. S. Sisodiya: identifying participants, confirming participants' diagnoses by chart review, data collection, drafting of manuscript. M. Walker: identifying participants, confirming participants' diagnoses by chart review, data collection, drafting of manuscript. D. Hesdorffer: analytic strategy development, statistical analysis, drafting of manuscript.

Study funding

This study was supported by the Sheffield Hospitals Charitable Trust.

Disclosure

The authors report no disclosures relevant to the manuscript. Go to Neurology.org/N for full disclosures.

Footnotes

  • Go to Neurology.org/N for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.

  • CME Course: NPub.org/cmelist

  • Received June 21, 2018.
  • Accepted in final form October 22, 2018.

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