Prediction of aspiration in dysphagia using logistic regression: oral intake and self-evaluation

Oropharyngeal dysphagia (OD) has a major influence on health in general and notably on health-related quality of life (HR-QoL) [1‐3].

Aspiration or silent aspiration in severe OD can cause aspiration pneumonia and, when accompanied by malnutrition and dehydration, can lead to hospitalization, intensive care admission or even death [2, 4]. The gold standards for detecting aspiration and silent aspiration are videofluoroscopic (VFSS) and fiberoptic endoscopic evaluation of swallowing (FEES). While both have a high sensitivity and specificity [5], they are invasive, may be burdensome for the patient and are expensive. Moreover, these gold standards are not generally available in clinical settings such as a nursing home or general practice.

Screening for OD can be performed in various ways such as by trial swallows using water or substances with different viscosities, by oxygen desaturation or by cough elicitation [6, 7]. Screening should be sufficiently sensitive and specific but also easy to administer without extensive training [6]. When patients fail the screening, further assessment of OD is recommended. Numerous assessments are available to evaluate OD in further detail. Each one is focussed on certain domains such as functional health status (FHS), health-related quality of life (HR-QoL) or oral intake.

The presence of aspiration is the most critical clinical sign in patients with OD. However, the literature [8, 9] reveals a moderate to low correlation between self-evaluation questionnaires and aspiration as determined by FEES or VFSS. Also, oral intake as assessed by the FOIS shows weak correlations with aspiration [10]. One study [11] determined the accuracy of the EAT-10 using VFSS as reference test, to detect aspiration in patients at risk of OD; although sensitivity was sufficient, low specificity was found (respectively, 83% and 25%).

Predictive modelling entails developing a mathematical tool that generates an accurate prediction [12]. Several studies on dysphagia have used predictive modelling to forecast swallowing problems based on various criteria: for example, dosimetric parameters in radiotherapy [13, 14], tumour size and location [14], VFSS parameters [15] or cervical auscultation [16]. The predicted outcome of the models ranged from radiotherapy-induced dysphagia [13, 14] to persistent dysphagia after stroke [15] and presence of aspiration [16]. The predicted outcome of most studies was dysphagia, though not differentiating between dysphagia with or without aspiration. To the best of our knowledge, no models thus far have used individual or combined self-evaluation questionnaires on FHS and/or HR-QoL in OD for predicting aspiration.

The purpose of this study was to build a predictive model that could forecast aspiration in patients with OD using oral intake status and commonly used self-evaluation questionnaires on FHS and HR-QoL.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

The purpose of this study was to build a prediction model for aspiration in patients with OD using common self-evaluation questionnaires and patients' oral intake status. Logistic regression modelling is the preferred method for this [33]. Herein, the number of parameters tested determines the size of the study population needed [34]. Both clinical experience and prior knowledge from the literature may be used to limit the number of predictors in such models. The variable selection was based on these assumptions. Good accuracy was found [35], i.e., AUC of 0.86 and 0.85 for variable sets A and B, respectively. Subsequently, LASSO regression showed excellent accuracy, i.e., AUC of 0.92 for selection of variables from both sets A and B. The resulting formula may be used in the future as a guide to predict aspiration in patients with OD. Our evaluation of internal consistency using cross-validation show that AUCs to be expected in repeat studies are somewhat lower and range between 0.74 and 0.77. The fact is that the smallest, predefined set of variables (B) resulted in the highest cross-validated AUC. This indicates that the relatively small sample size limits the number of predictors that can be used. The cross-validated accuracy for the set B and the LASSO model was almost identical (0.77), indicating that the LASSO is a viable alternative to logistic regression.

This study has some limitations. First, the subjects had high FOIS scores, i.e., no functional impairments in oral intake or only mild impairments. This may have influenced our findings and the generalizability of the results. In view of that possibility, a consecutive series of patients was included to avoid selection bias. As such, our population forms a representative sample of persons visiting an outpatient clinic for dysphagia in an academic hospital setting. Second, only details on the LASSO regression model using the set of variables A are presented here. However, as the AUCs were almost equal for both sets (A and B) when using LASSO regression modelling, the option of using set B in daily clinics might be considered as well. Clinicians may prefer to use DHI subscales rather than adding the SWAL-QoL subscales and VAS scales on Severity and Impairment. Future studies may consider such clinical preferences when building regression models. Third, as self-report measures target patient populations with adequate comprehensive reading skills, self-report measures may not be appropriate for patient populations with severe cognitive deficits such as persons with dementia or acquired brain injury. Still, self-report is considered an important part of the multidimensional assessment of dysphagia [3, 36] and, therefore, should be included in the assessment of OD when possible. Fourth, to reduce patient-experienced burden of assessment and facilitate data collection, patients in the waiting room may be asked to complete all self-report items directly into a computer or digitized system (e.g., mobile phone app or web application) while waiting for their clinician’s appointment, after which the chance of aspiration based on the final predictive model will be determined automatically. Further, the use of computerized data collection may support implementation of the predictive model in daily clinical practice; when implementing the model, only selected items need to be included to save time and reduce patient burden: five (out of eleven) SWAL-QoL subscales plus six other items (from different questionnaires). Fifth, external validation of the findings in another group of patients with OD was not performed. Nonetheless, all predictive models showed good to excellent accuracy with all AUC ≥ 0.85 and AUC exceeding 0.92 when using LASSO regression. Even though no great differences are expected when other groups of patients with OD are included, it may be interesting to compare the current results based on a patient population showing diversity in underlying medical conditions (e.g., head and neck cancer, neurological disorders such as stoke and Parkinson’s disease), with those from future studies in more homogeneous populations with OD. Casting the net wider might reveal similarities or discrepancies in study outcomes; to what degree our predictive model can be generalised to other patient populations remains to be evaluated in follow-up research. Our model can be considered a first step towards the assessment of aspiration risk in patients with OD using oral intake and self-report questionnaires only. The high accuracy of the final prediction model seems to make this a very promising avenue.

These findings are relevant for clinical practice and underscore the importance of self-reported evaluations in the clinical assessment of patients with OD. Until now, these questionnaires were used to measure concepts such as FHS and HR-QoL. They were not used for decision-making; specifically, they were not used to determine whether a patient with dysphagia was at risk for aspiration. The current study suggests that in the absence of gold standard measures, an accurate risk assessment can be performed on the grounds of combined oral intake and self-reported FHS and HR-QoL. This predictive model determines the chance of aspiration, not just a ‘pass or fail’ outcome, with excellent performance (AUC 0.92). This shows the additional value compared to a screening for dysphagia such as water swallow test with a nominal outcome (pass/fail). Possibly, future studies may address the usefulness of the current assessment protocol in clinical settings such as nursing homes or general practices where access to VFSS or FEES may still be limited, in contrast to the widespread availability in tertiary centres nowadays. Automated calculation using a web-based application or mobile app will improve feasibility of the model. In addition, to increase the robustness of the model, reducing the number of variables should be studied, however, at the cost of predictive performance. Furthermore, the use of a non-instrumental assessment protocol to identify aspiration in patients with OD may reduce costs in healthcare.

This study shows that aspiration in patients with OD may be predicted by a cost-effective, simple and non-invasive assessment protocol including oral intake status and patient self-evaluation questionnaires on FHS and HR-QoL. A predictive model was built using data from a consecutive series of patients at an outpatient clinic of a tertiary care centre. This model may be used to predict aspiration in patients with OD.