Can patient-reported outcome measures predict mortality in neurological populations? A systematic review

Abstract

Background:

Patient-reported outcome measures (PROMs) are increasingly used for symptom monitoring and care delivery, yet their prognostic value for identifying patients at higher risk for mortality in neurological populations is unclear. This systematic review evaluated whether PROMs predict mortality and/or survival in adults with neurological conditions.

Methods:

We systematically searched MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials (January 2002–November 2024) for studies incorporating PROMs into mortality or survival prediction models across 10 neurological conditions: motor neuron disease, diabetic neuropathy, nervous system cancers, Alzheimer’s and other dementias, Guillain–Barré syndrome, epilepsy, headache, multiple sclerosis, Parkinson’s disease, and stroke. Screening, data extraction, and risk-of-bias assessment followed the CHARMS and PRISMA guidelines. Findings were descriptively summarized.

Results:

Of 6,218 abstracts reviewed, 49 studies met the inclusion criteria. Most evaluated stroke (n = 16), nervous system cancers (n = 14), or motor neuron disease (n = 9). None evaluated headache, diabetic neuropathy, Guillain–Barré syndrome, or epilepsy. Of the included studies, 26 used generic PROMs, 19 used condition-specific PROMs, and 4 included both. Across conditions, PROMs independently predicted mortality in three-quarters of studies, with the strongest evidence observed in nervous system cancers and motor neuron disease. By instruments, EORTC QLQ in brain cancers and SF-36 in stroke showed the most consistent prognostic utility. Among studies with mixed findings by domain, physical health components were more likely to predict mortality than emotional components.

Conclusion:

PROMs independently predict mortality in several neurological conditions, though prognostic value varied by condition and instrument type. Future studies should evaluate their additive value and feasibility for integration into prognostic models in routine care.

1 Introduction

Neurological disorders represent a major global health burden, affecting an estimated 3.40 billion individuals in 2021 and contributing to approximately 11.1 million deaths annually (1). Collectively, they rank among the top contributors to global disability-adjusted life years and years of life lost, second only to cardiovascular diseases (1). Early identification of clinical decline and prognosis is critical for supporting patient-centered decision-making. Patient-reported outcomes (PROs), which capture patients’ subjective assessments of their health without clinician interpretation, are gaining recognition as valuable tools in this process (2–4).

Patient-reported outcome measures (PROMs) are increasingly integrated into routine clinical practice across various healthcare systems (5–7). They support diverse functions, including symptom screening, evaluating the impact of disease or treatment, auditing the quality of care, measuring healthcare utilization, and facilitating doctor–patient communication (4, 6, 8–12). In addition to these applications, PROMs have demonstrated prognostic value for hard clinical endpoints, such as mortality, in cardiology (13–16), oncology (17, 18), and other chronic illnesses (2). However, their prognostic value for mortality in neurological conditions remains less well established. For instance, a prior systematic review on the use of self-rated health (SRH) in stroke populations reported mixed findings regarding its association with survival (19). Furthermore, heterogeneity in PROM instruments and statistical analytical approaches across neurological studies presents challenges in evaluating their prognostic value.

The objective of this study was to evaluate the ability of PROMs to predict mortality and/or survival in neurological populations. The aims of our study were to systematically review published peer-reviewed articles describing predictive models for mortality and/or survival involving PROMs and to summarize their findings, including the types of PROMs used, the predictive ability of the PROMs, and the challenges and opportunities for informing the implementation of these emerging models in healthcare settings.

2 Methods

This review was registered in PROSPERO (CRD420250617247) and follows the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (20).

2.1 Data sources and searches

A comprehensive search strategy was developed in collaboration with a medical librarian to systematically search relevant literature in MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials on 11 November 2024 (Appendix 1). Additionally, the reference lists of included studies were reviewed to identify any missed journal articles.

The search strategy focused on three primary concepts: (1) mortality and/or survival, (2) PROMs, and (3) predictive models. For mortality and/or survival, we included terms for in-hospital mortality, 30-day mortality, 90-day mortality, overall mortality, survival, recurrence-free survival, relapse-free survival, and/or progression-free survival. PROM-related terms encompassed patient- or self-reported outcomes and proxy-reported outcomes, including health-related quality of life, quality of life, health utility, and/or condition-specific measures. Finally, terms for predictive models included predictive models, algorithms, artificial intelligence, machine learning, informatics, risk prediction, statistical models, and/or clinical decision support.

2.2 Eligibility criteria

The criteria for including studies in this research were based on the following PICOTS framework: P (Population), I (Intervention), C (Comparator), O (Outcomes), T (Time), and S (Study Design).

2.2.1 Population

Adult patients (≥18 years of age) with a neurological condition, as defined by the global burden of neurological disease, were included in this review (1). Infectious, traumatic, congenital, and developmental disorders were excluded. The review focused on the following 10 neurological conditions: (1) motor neuron disease (amyotrophic lateral sclerosis, primary lateral sclerosis, progressive bulbar palsy, progressive muscular atrophy), (2) diabetic neuropathy, (3) nervous system cancers (including brain tumors, peripheral nerve tumors, and spinal cord tumors), (4) Alzheimer’s and other dementias (vascular dementia, Alzheimer’s dementia, Lewy body dementia, frontotemporal dementia, and mild cognitive impairment), (5) Guillain–Barré syndrome, (6) epilepsy, (7) headache (migraine and tension-type headache), (8) multiple sclerosis, (9) Parkinson’s disease, and (10) stroke (ischemic, intracerebral hemorrhage, subarachnoid hemorrhage, and transient ischemic attack).

2.2.2 Intervention/exposure

PROMs were considered the intervention or exposure of interest.

2.2.3 Comparison

No specific comparator was required.

2.2.4 Outcomes

Survival and/or mortality were the outcomes of interest.

2.2.5 Time

A study was eligible for inclusion if it was published in English between 1 January 2002 and 8 November 2024, with an available full text. The 2002 start date was selected based on the increased adoption of PROMs following the publication of “Crossing the Quality Chasm: A New Health System for the 21st Century” by the Institute of Medicine Committee, which emphasized patient-centeredness as one of the six aims of quality healthcare (21). This led regulatory bodies such as the FDA and EMA to recognize PROMs, which were previously limited and infrequently used in healthcare, and to promote their integration into clinical research and healthcare delivery.

2.2.6 Study design

Eligible studies were required to describe a model predicting mortality and/or survival by incorporating PROMs in patient populations with selected neurological conditions. All model types were considered, including Cox proportional hazards regression, logistic regression, random forests, and neural network architectures.

2.2.7 Exclusion criteria

Studies focusing on individuals aged under 18 years were excluded.

2.3 Study selection and data extraction

Abstracts were screened using Covidence (Covidence systematic review software, Veritas Health Innovation, Melbourne, Australia) by two independent reviewers drawn from a pool of four reviewers (MA, YL, NT, and/or BL) (22). Conflicting decisions were evaluated and resolved by group consensus (MA, YL, NT, and BL). Full-text screening was then conducted similarly by two independent reviewers, with any conflicts resolved by group consensus.

Following full-text screening, the remaining publications were eligible for data extraction. A data extraction template was developed based on the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist (23), including source of data, participant description (including recruitment method), outcomes to be predicted, candidate predictors, sample sizes, missing data, model development (methods, assumptions, predictor selection, shrinkage), model performance (calibration, discrimination, classification measures), results (model coefficients), interpretation (including clinical relevance and future directions), and discussion of generalizability and limitations. Additional items were included to describe the PROM, its categorization, and the statistical analyses. The extraction sheet was revised to account for heterogeneity in study types, and comment boxes were included to provide an overall summary of the study findings (Supplementary Table 1).

Two independent reviewers extracted publication information into Covidence, with the second reviewer comparing extraction fields and finalizing consensus. Discrepancies were resolved by group consensus during weekly meetings. During extraction, relevant references were checked, and manuscripts were manually added to the full-text screening stage as applicable.

Risk of bias assessments were conducted similarly by two independent reviewers, with the second reviewer comparing extraction fields and determining consensus; any discrepancies were discussed and finalized during weekly group meetings. Risk of bias was based on an adapted version of the CHARMS checklist (23) (Supplementary Table 2).

2.4 Synthesis methods

Given the anticipated small number of eligible studies and substantial heterogeneity in how PROMs were measured and incorporated into prediction models, we summarized the results using descriptive statistics and narrative synthesis. We defined PROMs as condition-specific if they were developed to assess aspects of health specific to the disease, and generic if they were designed to assess general aspects of health across conditions (see Supplementary Table 3 for a full list of PROMs with classification).

The primary focus was not on effect measures but on whether the PROM was significantly associated with mortality or survival. When possible, the results were stratified by neurological condition to account for expected variability across disease groups.

3 Results

3.1 Overview of included articles

Of the 6,218 identified studies, 525 studies were removed as duplicates. Title and abstract screening of the remaining 5,693 studies yielded 189 studies for full-text review, of which 49 met the inclusion criteria (Figure 1). The included studies spanned a broad range of geographic regions, with the majority conducted in the United States (n = 13), in the United Kingdom (n = 6), and across multiple countries (n = 6). The most frequently studied neurological conditions were stroke (n = 16), nervous system cancers (n = 14), and motor neuron disease (n = 9). The number of participants who completed PROMs in the included studies ranged from 57 to 3,667. Of the included studies, 30 were prospective cohort studies, 12 were randomized controlled trials (RCTs), 5 were retrospective cohort studies, and 1 was a systematic review. One study pooled individual patient data from six RCTs and two prospective cohort studies, all of which were assessed using the same methods (24).

Figure 1

3.2 Overview of PROMs administered

The characteristics of the PROMs used across studies are summarized in Tables 1, 2. Overall, 26 studies used generic instruments, 18 used condition-specific instruments, and 5 studies used both. The most commonly used generic instruments were the 36-Item Short-Form Survey (SF-36) (n = 8) and the SRH item (n = 6) (Supplementary Table 3). Among condition-specific instruments, the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire and Brain Cancer Module (EORTC QLQ-C30/BN20) (n = 7), commonly used in brain cancer populations, and the Amyotrophic Lateral Sclerosis Functional Rating Scale—Revised (ALSFRS-R) (n = 4), commonly used in ALS, were the most frequently reported (Supplementary Table 3). PROMs were assessed at multiple time points in only 14 studies, and 7 studies evaluated the association between changes in PROMs over time and mortality.

3.3 Aims and methods

A total of 6 (12.2%) studies aimed to develop a prediction model, and 43 (87.8%) examined the association between PROMs and survival without the intent to develop a prediction model. Of the six studies that aimed to develop a prediction model, five specifically aimed to address whether PROMs could predict mortality. Most studies (n = 44) used Cox proportional hazards regression. In contrast, four studies used logistic regression, two studies used a Weibull survival model, and one study used Markov multistate modeling (Table 3).

3.4 Prediction of mortality

A total of 38 studies conducted univariable analyses, 43 conducted multivariable analyses, and 34 conducted both (Tables 1 and 2). Significant associations between PROMs and mortality were reported in 34 of 38 studies (89.5%) in univariable models and in 33 of 43 studies (76.7%) using multivariable models. Among the 34 studies that performed both univariable and multivariable analyses, 5 (14.7%) found significant associations in univariable analysis that were no longer significant after multivariable adjustment (25–29). One study reported significant variation by model type in multivariable analysis (30). In contrast, others reported variation by the presence of a lacunar infarct (31), depression (32), and cognitive impairment level (33). Age also influenced the magnitude of effect in at least one study (34).

In univariable analyses, 18 of 21 studies (85.7%) evaluating generic instruments and 18 of 20 (90.0%) evaluating condition-specific instruments reported significant associations with mortality (Table 1). These patterns were slightly attenuated in multivariable models: 19 of 27 studies (70.4%) evaluating generic instruments and 16 of 20 (80.0%) evaluating condition-specific instruments reporting significant associations (Table 1). PROMs independently predicted mortality in 11 of 13 studies on nervous system cancer, 10 of 14 on stroke, 6 of 7 on motor neuron disease, 4 of 5 on dementia, 1 of 3 on Parkinson’s disease, and 1 study on multiple sclerosis. Among the dementia studies that reported significant findings, two used proxy-reported PROMs (32, 35), one showed variation by the level of cognitive impairment (33), and another allowed either patient or proxy respondents (24). Notably, one study that did not find significant associations relied solely on patient-reported data (36). The instruments that most frequently predicted mortality in multivariable analyses were the EORTC QLQ for nervous system cancer (n = 7), the SF-36 for stroke (n = 6), and the ALSFRS-R for motor neuron disease (n = 2). Of seven studies that evaluated the associations between changes in PROMs over time and mortality, six reported significant associations in populations with nervous system cancer (n = 3), motor neuron disease (n = 2), and multiple sclerosis (n = 1).

Fifteen studies demonstrated inconsistent patterns in PROM’s associations with mortality, with only select instruments or subscores showing independent predictive value (Table 3 and Supplementary Table 4). After categorizing the PROM constructs for studies with mixed findings, 75.0% (9/12) of examined physical health measures or subscores were significantly associated with mortality, compared with 23.1% (3/13) of the emotional health measures and 66.7% (8/12) of other mental health–related subscores.

3.5 Quality assessment

The quality and risk of bias were assessed using a modified CHARMS checklist (Figure 2). The majority of studies had low risk of bias based on source of data (n = 43, 87.8%), definition and method of measurement of survival/mortality (n = 46, 93.9%), and the validity of the PROM used (n = 39, 79.6%). Twenty studies (40.8%) had a high risk of bias in how they handled predictors in the model, with many studies categorizing PROMs without using established cut-points (i.e., median splits). Although 14 (28.6%) studies failed to report missing data, 23 (46.9%) demonstrated a high risk of bias in handling missing data in their models, with many using complete-case analysis without discussing how missing data could affect estimates and interpretation. Model assumptions were not reported in 33 (67.4%) studies; most studies had a low risk of bias in both the selection of predictors for multivariable models (n = 27, 55.1%) and the selection of final predictors in the models (n = 32, 65.3%). Finally, as most models were not prediction models, the questions related to assessing the quality of model performance and validation were not applicable.

Figure 2

4 Discussion

This systematic review is the first to comprehensively evaluate the prognostic value of PROMs in predicting mortality across various neurological conditions, mapping the current evidence on their use as predictors of survival in these patient populations. The review was originally designed to assess prediction models incorporating PROMs for potential integration into EHR systems to identify patients with neurological conditions at increased risk of mortality. However, only five studies aimed to develop a prediction model for clinical prognostication (37–41). Of these, two involved patients with nervous system cancer. They found only modest improvement in discrimination metrics (i.e., the C-index) when PROMs were added to established clinical prognostic factors, suggesting limited incremental value in some contexts (40–44). The others found that PROMs provided significant predictive information and supported their use in future clinical applications. None of the models were implemented clinically or into the EHR, and none provided information on challenges or opportunities for implementation, leaving many of our initial questions unanswered.

Rather than constructing prediction models, the majority of studies in this systematic review sought to identify independent associations between PROMs and mortality. Among studies that performed multivariable analyses, approximately three-quarters found that PROMs were independently associated with mortality. The strongest evidence for prognostic utility emerged in nervous system cancers, where condition-specific instruments, such as the EORTC QLQ, consistently and independently predicted mortality. Similarly, most studies on motor neuron disease demonstrated strong independent prognostic value, typically using condition-specific PROMs such as ALSFRS-R, with one study suggesting that its effect on overall survival may be mediated by faster progression through disease stages, each associated with increasing mortality risk (45). It is worth noting that most studies used clinician-reported ALSFRS-R; therefore, this systematic review included only a limited number of studies that employed the patient-reported version of ALSFRS-R, with two studies using the PRO-ACT database that included a mix of patient- and clinician-entered ALSFRS-R assessments (37, 45).

In contrast, findings were more variable in stroke populations, particularly by instrument type. Six studies evaluating the SF-36 demonstrated significant associations; three studies focused solely on physical function and symptoms (46–48), while two studies reported significance for the overall SF-36 scores and physical function but not for emotional health assessed by the SF-36 or HADS (49, 50). Other PROMs that incorporated physical components, such as self-perceived fatigue and AQoL, also demonstrated prognostic value (51, 52). Conversely, instruments lacking physical components, such as SRH, CES-D, and the self-reported acute psychological stress questionnaire, were consistently non-significant (28, 46, 53–56). Interestingly, age appeared to modify these associations: Ayerbe et al. (34) found depression (measured by HADS) to be significantly associated with mortality in stroke patients younger than 65 years, but not in older individuals. Most stroke study participants were older than those in nervous system cancer or ALS studies, except for one study limited to individuals under 50 (49). Prior research has suggested that older individuals may evaluate their health differently, often providing more favorable self-ratings that correlate less strongly with clinical measures and exhibit reduced predictive validity for mortality with advancing age (56–59). Consequently, the mixed associations observed in stroke may reflect a combination of impaired insight, altered health perceptions, and the overwhelming influence of acute cerebrovascular events on prognosis (28). It is also worth noting that our review identified a lack of condition-specific PROMs for cerebrovascular conditions, as none of the included studies utilized stroke-specific PROMs to predict mortality (52, 60). Disease-specific instruments with greater emphasis on physical function and symptom burden may enhance prognostic utility in this population.

Choice of PROMs may also be critical for mortality prediction in neurological diseases. Condition-specific instruments demonstrated stronger associations, with 80.0% of relevant studies reporting significant associations with mortality, compared with 70.4% of studies evaluating generic instruments. Condition-specific instruments are more likely to focus on physical symptoms and functioning than generic instruments. Of the studies evaluating separate dimensions, physical components were more likely to be associated with mortality than emotional health components, including depression, anxiety, and stress. Prior studies have likewise shown that self-rated physical health is more predictive of mortality than mental health, with a stronger association observed in men than in women (61, 62). Furthermore, one study found that despite moderate to high correlations between similar subscales of two HRQoL instruments, their prognostic value differed, suggesting that it may be prudent to assess disease impact using more than one scale when the primary goal of PROM collection is mortality prediction (49).

Finally, cognitive impairment may also impact the prognostic value of PROMs. Individuals with greater cognitive impairment were more likely to rate their health positively (36), and proxy-reported PROs, but not patient-reported PROs, predicted mortality in patients with dementia (35). Similarly, Walker et al. (33) observed that the predictive value of PROs declined with greater cognitive impairment, potentially reflecting reduced capacity to integrate less immediate factors into their overall health assessment (33). These findings suggest that future studies may benefit from incorporating cognitive function into evaluations of the prognostic value of PROs and from considering proxy reporting when appropriate. In addition, as noted in the risk-of-bias assessment, missing data posed a significant concern in interpreting study results. Missing data due to PROMs were reported to be more common among patients with cognitive impairment in a study evaluating the prognostic value of PROs in brain cancers (39). This pattern may partly be attributable to the mode of PROM delivery, which was observed to affect data completeness, with disease burden and physical and cognitive capacity suggested as potential contributing factors (63). Given the demonstrated predictive value of proxy-reported PROs in populations with dementia and cognitive impairment, broader use of proxy reporting, along with careful consideration of PROM delivery methods, could enhance data completeness and reliability.

Several limitations of our review should be considered. First, only studies published after 2002 were included, as the use of PROs in healthcare was relatively uncommon before the Institute of Medicine highlighted patient-centeredness as one of the six aims for quality of healthcare (21). Although earlier literature demonstrated significant associations between PROs, particularly SRH, and mortality across different populations (64–66), these studies did not evaluate prediction models for use in large healthcare settings. Second, limiting inclusion to full-text articles may have introduced selection bias; however, this was necessary given the impracticality of screening more than 55,000 records otherwise. Nevertheless, a thorough review of references was conducted, and 16 additional manuscripts not captured during initial screening were included.

The heterogeneity in study design, PROM instruments, timing of administrations, and statistical methods precluded quantitative synthesis in a meta-analysis. This heterogeneity likely reflects differences in disease mechanisms and clinical trajectories across neurological conditions, as well as variations in the locations and eras in which studies were conducted, during which the selection, availability, and popularity of PROM instruments may have differed. Consequently, the evidence was synthesized using a descriptive approach stratified primarily by neurological condition, highlighting an important opportunity to improve comparability across future studies by standardizing PROM selection and administration within and across neurological conditions.

Several limitations of the included studies were also identified through the CHARMS-based risk-of-bias assessment. The majority of studies did not explicitly report evaluation of key modeling assumptions, such as proportional hazards, and were therefore conservatively rated as not meeting these criteria. Similarly, handling of missing data was frequently insufficiently reported, with many studies relying on complete-case analysis or providing no description of how missing data were handled. Substantial variation was also observed in predictor handling, including whether PROMs were modeled as continuous variables or categorized using established, validated, or otherwise justified cut-points, as well as in the transparency of predictor selection for inclusion in multivariable models. However, most included studies were not designed as formal prediction model studies but rather aimed to examine prognostic associations between PROMs and mortality; therefore, comprehensive reporting of model assumptions, performance metrics, and validations may have been outside their stated objectives. Nevertheless, future studies seeking to translate PROMs into clinically useful prognostic tools would benefit from greater adherence to established reporting guidelines to improve transparency and interpretability, thereby strengthening the overall evidence base.

No studies evaluated the association between PROs and mortality in four of the included neurological conditions (i.e., headache, diabetic neuropathy, Guillain–Barré syndrome, and epilepsy), limiting the generalizability across the full spectrum of neurological diseases and highlighting an important gap for future research on the prognostic value of PROMs. Finally, no studies evaluated predictive models implemented in healthcare settings, which was one of the original aims of this systematic review. Further research is needed to elucidate challenges and opportunities for integrating these emerging models into clinical practice. Despite these limitations, this review provides a comprehensive descriptive overview of PROMs used to predict mortality across a wide range of neurological conditions.

5 Conclusion

Overall, this systematic review found that PROMs independently predict mortality, particularly in nervous system cancers and motor neuron disease. In contrast, findings were more variable in stroke and dementia depending on the PROM instruments administered, where factors such as cognitive impairment, altered health perceptions, lack of condition-specific instruments, and age may influence prognostic performance. These results underscore the importance of selecting appropriate PROMs, accounting for relevant patient characteristics, and incorporating proxy reporting when necessary. Furthermore, no eligible studies evaluated the prognostic value of PROMs for mortality in epilepsy, headache, Guillain–Barré syndrome, or diabetic neuropathy, highlighting important gaps in the current evidence base. PROMs may serve as valuable tools for screening and monitoring patients’ symptoms and as indicators of mortality risk. As the use of PROMs in neurological care continues to expand, future studies should extend the evaluation of their prognostic value for mortality to currently underrepresented neurological conditions and rigorously assess their added prognostic value beyond traditional clinical predictors.

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