Dataset Information

Patient profiled data for treatment decision-making: valuable as an add-on to hepatitis C clinical guidelines?

ABSTRACT:

Background and aims

Systematic reviews and medical guidelines are widely used in clinical practice. However, these are often not up-to-date and focussed on the average patient. We therefore aimed to evaluate a guideline add-on, TherapySelector (TS), which is based on monthly updated data of all available high-quality studies, classified in specific patient profiles.

Methods

We evaluated the TS for the treatment of hepatitis C (HCV) in an international cohort of patients treated with direct-acting antivirals between 2015 and 2020. The primary outcome was the number of patients receiving one of the two preferred treatment options of the HCV TS, based on the highest level of evidence, cure rate, absence of ribavirin-associated adverse effects, and treatment duration.

Results

We enrolled 567 patients. The number of patients treated with one of the two preferred treatment options according to the HCV TS ranged between 27% (2015) and 60% (2020; p < 0.001). Most of the patients received a regimen with a longer treatment-duration (up to 34%) and/or addition of ribavirin (up to 14%). The effect on the expected cure-rate was minimal (1-6% higher) when the first preferred TherapySelector option was given compared to the actual treatment.

Conclusions

Medical decision-making can be optimised by a guideline add-on; in HCV its use appears to minimise adverse effects and cost. The use of such an add-on might have a greater impact in diseases with suboptimal cure-rates, high costs or adverse effects, for which treatment options rely on specific patient characteristics.

SUBMITTER: Brakenhoff SM

PROVIDER: S-EPMC11321176 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

Patient profiled data for treatment decision-making: valuable as an add-on to hepatitis C clinical guidelines?

Brakenhoff Sylvia M SM Theijse Thymen T van Wijngaarden Peter P Trautwein Christian C Brozat Jonathan F JF Tacke Frank F Honkoop Pieter P Vanwolleghem Thomas T Posthouwer Dirk D Zeuzem Stefan S Mihm Ulrike U Wedemeyer Heiner H Berg Thomas T Schalm Solko W SW de Knegt Robert J RJ

BMC medical informatics and decision making 20240813 1

<h4>Background and aims</h4>Systematic reviews and medical guidelines are widely used in clinical practice. However, these are often not up-to-date and focussed on the average patient. We therefore aimed to evaluate a guideline add-on, TherapySelector (TS), which is based on monthly updated data of all available high-quality studies, classified in specific patient profiles.<h4>Methods</h4>We evaluated the TS for the treatment of hepatitis C (HCV) in an international cohort of patients treated wi ...[more]

PMID: 39138441

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Project description:Objectives/hypothesisTo explore possible factors that might impact a patient's choice to pursue endoscopic sinus surgery (ESS) or continue with medical management for treatment of refractory chronic rhinosinusitis (CRS).Study designCross-sectional evaluation of a multicenter prospective cohort.MethodsTwo hundred forty-two subjects with CRS were prospectively enrolled within four academic tertiary care centers across North America with ongoing symptoms despite prior medical treatment. Subjects either self-selected continued medical management (n = 62) or ESS (n = 180) for treatment of sinonasal symptoms. Differences in demographics, comorbid conditions, and clinical measures of disease severity between subject groups were compared. Validated metrics of social support, personality, risk aversion, and physician-patient relationships were compared using bivariate analyses, predicted probabilities, and receiver operating characteristic curves at the 0.05 alpha level.ResultsNo significant differences were found between treatment groups for any demographic characteristic, clinical cofactor, or measure of social support, personality, or the physician-patient relationship. Subjects electing to pursue sinus surgery did report significantly worse average quality-of-life (QOL) scores on the 22-item Sinonasal Outcome Test (SNOT-22; P < .001) compared to those electing continued medical therapy (54.6 ± 18.9 vs. 39.4 ± 17.7), regardless of surgical history or polyp status. SNOT-22 score significantly predicted treatment selection (odds ratio, 1.046; 95% confidence interval, 1.028-1.065; P < .001) and was found to accurately discriminate between subjects choosing endoscopic sinus surgery and those electing medical management 72% of the time.ConclusionsWorse patient-reported disease severity, as measured by the SNOT-22, was significantly associated with the treatment choice for CRS. Strong consideration should be given for incorporating CRS-specific QOL measures into routine clinical practice.Level of evidence2b.

Project description:Background. Accurate diagnosis of patients' preferences is central to shared decision making. Missing from clinical practice is an approach that links pretreatment preferences and patient-reported outcomes. Objective. We propose a Bayesian collaborative filtering (CF) algorithm that combines pretreatment preferences and patient-reported outcomes to provide treatment recommendations. Design. We present the methodological details of a Bayesian CF algorithm designed to accomplish 3 tasks: 1) eliciting patient preferences using conjoint analysis surveys, 2) clustering patients into preference phenotypes, and 3) making treatment recommendations based on the posttreatment satisfaction of like-minded patients. We conduct a series of simulation studies to test the algorithm and to compare it to a 2-stage approach. Results. The Bayesian CF algorithm and 2-stage approaches performed similarly when there was extensive overlap between preference phenotypes. When the treatment was moderately associated with satisfaction, both methods made accurate recommendations. The kappa estimates measuring agreement between the true and predicted recommendations were 0.70 (95% confidence interval = 0.052-0.88) and 0.73 (0.56-0.90) under the Bayesian CF and 2-stage approaches, respectively. The 2-stage approach failed to converge in settings in which clusters were well separated, whereas the Bayesian CF algorithm produced acceptable results, with kappas of 0.73 (0.56-0.90) and 0.83 (0.69-0.97) for scenarios with moderate and large treatment effects, respectively. Limitations. Our approach assumes that the patient population is composed of distinct preference phenotypes, there is association between treatment and outcomes, and treatment effects vary across phenotypes. Findings are also limited to simulated data. Conclusion. The Bayesian CF algorithm is feasible, provides accurate cluster treatment recommendations, and outperforms 2-stage estimation when clusters are well separated. As such, the approach serves as a roadmap for incorporating predictive analytics into shared decision making.

Project description:Rationale & objectivePatient education and decision support tools could facilitate decisions around the timing of antiviral therapy in patients living with both hepatitis C virus (HCV) infection and chronic kidney disease (CKD). We previously developed a tool through the HELP (Helping Empower Liver and Kidney Patients) study. This article evaluates the preliminary efficacy and usability of the tool among participants with both HCV infection and CKD.Study designPre-post study pilot evaluation.Setting & participantsParticipants were at least 18 years old, were English speaking, and had a diagnosis of chronic HCV infection and CKD; they were seen in CKD clinics, dialysis units, and/or hepatology and liver transplantation clinics.InterventionElectronic patient decision support tool.OutcomesParticipants' change in knowledge, certainty about choice, decision self-efficacy, patients' treatment preferences, and tool usability.Results70 participants were recruited; 56 of 70 (80.0%) completed study procedures. Nearly all (51/56; 91.1%) requested paper-based study procedures despite the electronic design of the tool. Participants reported that they were most worried about the following treatment factors: (1) cost of drugs to treat HCV infection, (2) how their HCV infection affected their CKD, and (3) wait times for a kidney transplant. After using the decision tool, participants had significantly higher HCV infection and CKD knowledge (mean posttest percent of questions answered correctly = 65.74% vs pretest percent of questions answered correctly = 53.44%; P < 0.001) and more certainty about choice (mean posttest = 3.13 vs pretest = 2.65; P = 0.05). There were no significant changes in decision self-efficacy (mean posttest = 86.62 vs pretest = 84.68; P = 0.48).LimitationsSingle-site pilot study to explore preliminary tool efficacy and usability.ConclusionsThis study suggests that a decision tool may support informed patient-centered choices among patients with HCV infection and CKD. Future studies should evaluate ways to improve care decisions in a larger sample using both paper-based and electronic materials.FundingMerck & Co, Inc, Kenilworth, NJ.Trial registrationRegistered at clinicaltrials.gov with study number NCT03426787.

Dataset Information

Patient profiled data for treatment decision-making: valuable as an add-on to hepatitis C clinical guidelines?

Background and aims

Methods

Results

Conclusions

Publications

Patient profiled data for treatment decision-making: valuable as an add-on to hepatitis C clinical guidelines?

Similar Datasets

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