Functional and Symptomatic Clinical Trial Endpoints

Psotka, Mitchell A.; Abraham, William T.; Fiuzat, Mona; Filippatos, Gerasimos; Lindenfeld, JoAnn; Ahmad, Tariq; Felker, G. Michael; Jacob, Richard; Kitzman, Dalane W.; Leifer, Eric; Lewis, Eldrin F.; Mentz, Robert J; Nkulikiyinka, Richard; Ni, Wei; Schaber, Daniel E.; Sharma, Abhinav; Solomon, Scott D.; Stockbridge, Norman; Teerlink, John R.; Unger, Ellis F.; Whellan, David J.; Wittes, Janet; Anker, Stefan D.; O’Connor, Christopher M

doi:10.1016/j.jchf.2022.09.012

Cited by 23 publications

(14 citation statements)

References 40 publications

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“…Missing data might also represent an important statistical issue when analysing data for research purposes, because data absence due to non‐response may not be random. The limited available evidence might support the use of multiple imputation methods for the handling of missing data 100–102 . However, in randomized controlled trials these techniques do not allow generation of robust conclusions if a substantial amount of data are missing 102 …”

Section: Use Of Patient‐reported Outcome In Heart Failure Clinical Pr...mentioning

confidence: 99%

“…The limited available evidence might support the use of multiple imputation methods for the handling of missing data 100–102 . However, in randomized controlled trials these techniques do not allow generation of robust conclusions if a substantial amount of data are missing 102 …”

Section: Use Of Patient‐reported Outcome In Heart Failure Clinical Pr...mentioning

confidence: 99%

“…The limited available evidence might support the use of multiple imputation methods for the handling of missing data. [100][101][102] However, in randomized controlled trials these techniques do not allow generation of robust conclusions if a substantial amount of data are missing. 102 One more limitation for PRO use from the physician's perspective has been shown to be the difficulties in their interpretation and choice, i.e.…”

Section: Use Of Patient-reported Outcome In Heart Failure Clinical Pr...mentioning

confidence: 99%

“…[100][101][102] However, in randomized controlled trials these techniques do not allow generation of robust conclusions if a substantial amount of data are missing. 102 One more limitation for PRO use from the physician's perspective has been shown to be the difficulties in their interpretation and choice, i.e. which PRO should be used, use of summary or domain-specific scores, what a specific score means.…”

Section: Use Of Patient-reported Outcome In Heart Failure Clinical Pr...mentioning

confidence: 99%

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Use of patient‐reported outcomes in heart failure: from clinical trials to routine practice

Savarese

Lindenfeld

Stolfo

et al. 2023

European J of Heart Fail

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Heart failure (HF) is a complex syndrome that affects mortality/morbidity and acts at different levels in the patient's life, resulting in a drastic impairment in multiple aspects of daily activities (e.g. physical, mental/emotional, and social) and leading to a reduction in quality of life. The definition of disease status and symptom severity has been traditionally based on the physician assessment, while the patient's experience of disease has been long overlooked. The active participation of patients in their own care is necessary to better understand the perception of disease and the multiple aspects of life affected, and to improve adherence to treatments. Patient‐reported outcomes (PROs) aim to switch traditional care to a more patient‐centred approach. Although PROs demonstrated precision in the evaluation of disease status and have a good association with prognosis in several randomized controlled trials, their implementation into clinical practice is limited. This review discusses the modalities of use of PROs in HF, summarizes the most largely adopted PROs in HF care, and provides an overview on the application of PROs in trials and the potential for their transition to clinical practice. By discussing the advantages and the disadvantages of their use, the reasons limiting their application in daily clinical routine, and the strategies that may promote their implementation, this review aims to foster the systematic integration of the patient's standpoint in HF care.

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Section: Use Of Patient‐reported Outcome In Heart Failure Clinical Pr...mentioning

confidence: 99%

Section: Use Of Patient‐reported Outcome In Heart Failure Clinical Pr...mentioning

confidence: 99%

Section: Use Of Patient-reported Outcome In Heart Failure Clinical Pr...mentioning

confidence: 99%

Section: Use Of Patient-reported Outcome In Heart Failure Clinical Pr...mentioning

confidence: 99%

See 2 more Smart Citations

Use of patient‐reported outcomes in heart failure: from clinical trials to routine practice

Savarese

Lindenfeld

Stolfo

et al. 2023

European J of Heart Fail

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“…Population‐average changes often do not differ by thresholds that are greater than the MCID for individual patients, which can make inferences about the magnitude of health status improvement with the interventions difficult to appreciate. Responder analysis, which describes the proportions of patients in each treatment arm who experience changes in their health status of varying clinical magnitudes, is one way of assessing clinical relevance in this setting, although there are arguments against this analytic approach too 13,14 . In particular, assessment at a single time point is poor evidence to classify a patient as a responder.…”

Section: Minimal Clinically Important Differencementioning

confidence: 99%

Patient‐reported outcome measures and patient engagement in heart failure clinical trials: multi‐stakeholder perspectives

Zannad¹,

Alikhaani²,

Alikhaani³

et al. 2023

European J of Heart Fail

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There are many consequences of heart failure (HF), including symptoms, impaired health‐related quality of life (HRQoL), and physical and social limitations (functional status). These have a substantial impact on patients' lives, yet are not routinely captured in clinical trials. Patient‐reported outcomes (PROs) can quantify patients' experiences of their disease and its treatment. Steps can be taken to improve the use of PROs in HF trials, in regulatory and payer decisions, and in patient care. Importantly, PRO measures (PROMs) must be developed with involvement of patients, family members, and caregivers from diverse demographic groups and communities. PRO data collection should become more routine not only in clinical trials but also in clinical practice. This may be facilitated by the use of digital tools and interdisciplinary patient advocacy efforts. There is a need for standardization, not only of the PROM instruments, but also in procedures for analysis, interpretation and reporting PRO data. More work needs to be done to determine the degree of change that is important to patients and that is associated with increased risks of clinical events. This ‘minimal clinically important difference’ requires further research to determine thresholds for different PROMs, to assess consistency across trial populations, and to define standards for improvement that warrant regulatory and reimbursement approvals. PROs are a vital part of patient care and drug development, and more work should be done to ensure that these measures are both reflective of the patient experience and that they are more widely employed.

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Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure

Adejumo,

Thangaraj,

Dhingra

et al. 2024

JAMA Netw Open

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ImportanceSerial functional status assessments are critical to heart failure (HF) management but are often described narratively in documentation, limiting their use in quality improvement or patient selection for clinical trials.ObjectiveTo develop and validate a deep learning natural language processing (NLP) strategy for extracting functional status assessments from unstructured clinical documentation.Design, Setting, and ParticipantsThis diagnostic study used electronic health record data collected from January 1, 2013, through June 30, 2022, from patients diagnosed with HF seeking outpatient care within 3 large practice networks in Connecticut (Yale New Haven Hospital [YNHH], Northeast Medical Group [NMG], and Greenwich Hospital [GH]). Expert-annotated notes were used for NLP model development and validation. Data were analyzed from February to April 2024.ExposuresDevelopment and validation of NLP models to detect explicit New York Heart Association (NYHA) classification, HF symptoms during activity or rest, and frequency of functional status assessments.Main Outcomes and MeasuresOutcomes of interest were model performance metrics, including area under the receiver operating characteristic curve (AUROC), and frequency of NYHA class documentation and HF symptom descriptions in unannotated notes.ResultsThis study included 34 070 patients with HF (mean [SD] age 76.1 [12.6] years; 17 728 [52.0]% female). Among 3000 expert-annotated notes (2000 from YNHH and 500 each from NMG and GH), 374 notes (12.4%) mentioned NYHA class and 1190 notes (39.7%) described HF symptoms. The NYHA class detection model achieved a class-weighted AUROC of 0.99 (95% CI, 0.98-1.00) at YNHH, the development site. At the 2 validation sites, NMG and GH, the model achieved class-weighted AUROCs of 0.98 (95% CI, 0.96-1.00) and 0.98 (95% CI, 0.92-1.00), respectively. The model for detecting activity- or rest-related symptoms achieved an AUROC of 0.94 (95% CI, 0.89-0.98) at YNHH, 0.94 (95% CI, 0.91-0.97) at NMG, and 0.95 (95% CI, 0.92-0.99) at GH. Deploying the NYHA model among 182 308 unannotated notes from the 3 sites identified 23 830 (13.1%) notes with NYHA mentions, specifically 10 913 notes (6.0%) with class I, 12 034 notes (6.6%) with classes II or III, and 883 notes (0.5%) with class IV. An additional 19 730 encounters (10.8%) could be classified into functional status groups based on activity- or rest-related symptoms, resulting in a total of 43 560 medical notes (23.9%) categorized by NYHA, an 83% increase compared with explicit mentions alone.Conclusions and RelevanceIn this diagnostic study of 34 070 patients with HF, the NLP approach accurately extracted a patient’s NYHA symptom class and activity- or rest-related HF symptoms from clinical notes, enhancing the ability to track optimal care delivery and identify patients eligible for clinical trial participation from unstructured documentation.

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Functional and Symptomatic Clinical Trial Endpoints

Cited by 23 publications

References 40 publications

Use of patient‐reported outcomes in heart failure: from clinical trials to routine practice

Use of patient‐reported outcomes in heart failure: from clinical trials to routine practice

Patient‐reported outcome measures and patient engagement in heart failure clinical trials: multi‐stakeholder perspectives

Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure

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