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Researchers often use surveys to learn about what patients prefer. The wording of survey questions may affect how patients answer.

In this study, the research team compared different ways of asking patients with type 2 diabetes questions in a national survey. The questions asked patients about managing their diabetes and the medicines they prefer. The team wanted to see how accurately the different ways of asking questions measured patients' preferences. The study looked at whether patients thought the different ways of asking questions:

• Were easy to understand and answer
• Led to answers that matched what patients really wanted

Patients with chronic health problems, such as diabetes, often need to change treatment plans over time to improve their health. To help with this process, doctors can monitor patients' health through follow-up clinic visits and lab tests. Doctors may also suggest changing a treatment plan in response to visits or lab test results. When a treatment plan changes in this way, it's called a dynamic treatment plan. In this study, the research team developed and tested new statistical methods to learn how dynamic treatment plans and choices about follow-up care affect patients' health. These methods use electronic health records, or EHRs. Using EHRs is helpful because they have data on

• What treatments patients have received over time
• How treatments have affected patients' health
• Follow-up information such as lab test results

But the data may differ for patients based on when and why they go to the doctor. These differences make it hard for researchers to accurately know the effect of dynamic treatment plans across many patients.

To access the methods and software, please visit the simcasual R Package.

Researchers can use data from health registries or electronic health records to compare two or more treatments. Registries store data about patients with a specific health problem. These data include how well those patients respond to treatments and information about patient traits, such as age, weight, or blood pressure. But sometimes data about patient traits are missing.

Missing data about patient traits can lead to incorrect study results, especially when traits change over time. For example, weight can change over time, and the patient may not report their weight at some points along the way. Researchers use statistical methods to fill in these missing data.

In this study, the research team compared a new statistical method to fill in missing data with traditional methods. Traditional methods remove patients with missing data or fill in each missing number with a single estimate. The new method creates multiple possible estimates to fill in each missing number.

To access the methods, software, and R package, please visit the SimulateCER GitHub and SimTimeVar CRAN website.

Clinical practice guidelines are recommendations for doctors about when and how to treat health problems. Guidelines are often based on research that compares the benefits and harms of different tests or treatments for one health problem. But this research doesn't always consider that people may have other health problems or different preferences. Guideline developers need to know what is important to patients.

In this study, the research team developed a process to inform development of clinical guidelines. The team wanted to learn how the balance of benefits and harms of treatment options changes when it includes patient preferences. In this new process, the team

• Defined questions comparing treatment options based on input from patients with three or more long-term health problems
• Used data from prior research studies to answer these questions and assess the balance of benefits and harms of treatment options
• Used results from a patient survey, looking to see if the balance of benefits and harms could change when patients have different preferences.

INtegrated Services for Pain: Interventions to Reduce Pain Effectively (INSPIRE) was a pragmatic randomized trial conducted from 2019 to 2023 with adults receiving chronic opioid therapy (COT) of at least 20 morphine milligram equivalents (MME) daily for chronic noncancer pain (CNCP). Participants were recruited from primary care and specialty pain clinics at three academic health centers in North Carolina and Tennessee. The study compared the effectiveness of the two behavioral interventions, 1) shared decision making (SDM) versus 2) motivational interviewing plus cognitive behavioral therapy for chronic pain (MI+CBT), on change in opioid dose, physical function, and pain interference. INSPIRE combined data from electronic health records (EHR) on opioid dose from baseline to 18 months and comorbidities with participant survey data at baseline, 6, and 12 months on the following topics:

• physical function,
• pain interference,
• pain intensity,
• anxiety,
• depression,
• pain severity,
• discontinuation of opioids,
• intent to reduce opioids,
• opioid use relative to baseline,
• adverse events,
• demographics,
• health insurance coverage,
• health literacy,
• patient-centered communication, and
• types of pain treatment used.

The collection includes three analysis datasets:

1. Adverse Events Dataset - one record per subject per adverse event
2. Opioid Prescriptions Dataset (post-processed opioid prescriptions used to derive the study's primary outcome) - one record per subject per opioid prescription
3. Outcomes Dataset (contains all of the study's demographics, primary, secondary, exploratory, and subgroup analysis variables) - one record per subject per timepoint

Patients' beliefs and expectations may affect how they respond to treatment. But these feelings are hard to measure.

In this study, the research team created a set of surveys called Healing Encounters and Attitudes Lists, or HEAL. HEAL helps researchers understand patients' beliefs and expectations about treatment. HEAL measures patients'

• Connections with their doctors and nurses
• Feelings about their doctor's office and staff
• Expectations about treatment
• Outlook on life
• Strength of spiritual beliefs
• Comfort with complementary and alternative medicine, or CAM
• The team also used the Patient-Reported Outcomes Measurement Information System, or PROMIS, to measure patients' pain, health, and function. PROMIS is a set of surveys researchers and doctors use for many diseases and treatments.

The team wanted to learn if HEAL could predict how patients respond to treatment for chronic pain. Chronic pain is pain that lasts for months or years. The team used HEAL and PROMIS to look at why some groups of patients respond differently to treatment for chronic pain. Patients got either conventional treatment, such as physical therapy or medicine, or CAM, such as acupuncture, chiropractic treatment, or massage.

The primary aim of this randomized clinical trial is to compare the effectiveness of the Specific Carbohydrate Diet (SCD) and the Mediterranean style diet (MSD) in inducing symptomatic and clinical remission in patients with Crohn's disease. Secondary objectives are to compare the effectiveness of the SCD and MSD in reducing mucosal and systemic inflammation, assessed by measuring the concentration of fecal calprotectin (FCP) and C-reactive protein (CRP) respectively; to compare the diets' effectiveness in improving fatigue, pain, and joint symptoms; and to determine the proportion of patients who continue study diets when prepared food is no longer provided without cost and their reasons for discontinuing the diets. The research aims were guided by crowdsourcing patient-generated research priorities; those that received the most support from Patient-Powered Research Network (PPRN) members were related to diet.

Based on the book Breaking the Vicious Cycle (Gottschall 1987), the Specific Carbohydrate Diet (SCD) restricts all but simple carbohydrates. Fresh fruits, vegetables, unprocessed meats, lactose-free cheeses, and certain legumes are permitted; grains, processed foods, canned foods, and milk are not permitted. The Mediterranean style diet (MSD) involves a high intake of olive oil, fruit, nuts, vegetables, and cereals; moderate intake of legumes, fish, seafood, and poultry; and low dairy intake. Red and processed meats, soda drinks, bakery foods, and sweets are not permitted. The MSD was selected as the alternative diet in this trial due to its easier implementation, consistency with U.S. Department of Agriculture and World Health Organization recommendations, and evidence of its role in overall health and specific benefits for Crohn's patients.

A total of 194 adult patients with mild to moderate Crohn's disease were enrolled and randomized into either the SCD (intervention) or MSD (control) diet groups at 33 different sites across the United States. Patients received meal delivery for their assigned diet for six weeks, then were provided instructions and recipes to adhere to the diet on their own for weeks seven through twelve. Outcome measures were taken at baseline, six weeks, and twelve weeks.

The Preserving Kidney Function in Children With Chronic Kidney Disease (PRESERVE) study was designed to provide new knowledge to inform shared decision-making regarding blood pressure (BP) management for pediatric chronic kidney disease (CKD). PRESERVE compared the effectiveness of alternative strategies for monitoring and treating hypertension on preserving kidney function; expanded the National Patient-Centered Clinical Research Network (PCORnet) Common Data Model by adding pediatric- and kidney-specific variables and linking electronic health record data to other kidney disease databases; and assessed the lived experiences of patients related to BP management.

Participants were recruited from 15 clinical institutions across the United States. The research team analyzed electronic health record (EHR) data from 11,851 children with CKD and their caregivers to compare different ways to monitor and treat BP to preserve kidney function. In addition, a subset of patients and caregivers completed an online survey detailing patient-reported outcomes, such as fatigue, life satisfaction, pain levels, sleep disturbance, anxiety, and peer relationships (n=395).

Due to the risk of re-identification based on unique patterns in the individual-level PCORnet electronic health record (EHR) data, patient privacy regulations prohibit the public release of the individual-level data. This collection contains the code underlying the analysis; instructions, codesets, and output lists for the PCORnet queries; and the survey questionnaires for patients and family members.

To diagnose rare genetic conditions, doctors look at patients' genetic data and a phenotypic profile. A phenotypic profile is a record of all the physical traits of a condition. It uses a list of standard terms called Human Phenotype Ontology, or HPO. Doctors and clinic staff do a thorough exam with the patient to create the profile. The exam takes a long time and often more than one visit.

Patients may be able to create phenotypic profiles themselves using surveys. These surveys may take less time than clinic visits. But it is unclear whether patient surveys can provide enough details to correctly identify conditions.

In this project, the research team tested two surveys:

• Phenotypr. This survey asks patients to describe their symptoms and then matches the descriptions to plain language HPO or clinical HPO terms.
• GenomeConnect. This survey uses multiple choice questions to asks patients about their health and symptoms.

One way to see if a treatment works is to compare data from people who received the treatment with data from those who didn't or who received a different treatment. But sometimes the ways that people differ, such as their age or other health problems, can bias results. For example, if the people who didn't get the treatment are older or sicker than people who did get the treatment, results could suggest that the treatment works better than it really does.

One way to avoid this type of bias is to use case-only study designs. Case-only studies compare each patient's health before and after treatment. But case-only studies often report the relative risk of a health event, such as stroke, among two groups of patients, instead of the absolute risk. For example, relative risk can show how the risk of stroke differs between patients who smoke and those who do not. Absolute risk would give the percentage of patients having a stroke among all patients. Absolute risk can help inform treatment decisions. But methods to measure absolute risk in case-only studies are limited. Also, clear guidance is lacking on how to best design and analyze a case-only study.

In this study, the research team created a guide and new methods for designing and analyzing case-only studies.

Current medical guidelines recommend a type of medicine called ACE/ARBs to help patients live longer and protect their kidneys after a stroke. But studies show that rates of kidney disease have gone up at the same time that more people have been using this medicine. Additional research may help show if some patients shouldn't take ACE/ARBs after a stroke.

In this study, the research team wanted to learn about the effects of taking ACE/ARBs for patients over age 65 who've had a stroke. The team reviewed Medicare claims for stroke survivors with and without chronic kidney disease, or CKD. CKD is a health problem in which the kidneys don't remove waste from the blood well. The team compared patients in areas of the country with different rates of ACE/ARB use. The team looked at how many patients lived and how many had kidney problems over two years.

Researchers can use data on patient traits such as age, health problems, and treatment preferences, to create personalized treatment rules, or PTRs. PTRs provide doctors with guidance on how to treat patients' health problems based on their traits. But PTRs based on a single data source may not apply to all patients. For example, if researchers create a PTR using data from older people with heart failure, it may not apply to younger people with heart failure.

To avoid this problem, researchers can create PTRs by combining data from many sources. PTRs based on many data sources can help guide treatment for patients with different traits.

In this study, the research team created and tested a new method for creating PTRs using data from multiple sources.

The Veterans' Pain Care Organizational Improvement Comparative Effectiveness (VOICE) Study was a 12-month pragmatic randomized comparative effectiveness trial. The target population was primary care patients (n=820) at ten sites within the United States Department of Veterans Affairs (VA) Health Care System with moderate to severe pain despite treatment with moderate or high-dose long-term opioid treatment (LTOT). The study's primary aim was to compare integrated pain team (IPT) versus pharmacist telecare collaborative management (TCM) for improving pain and reducing opioid use among patients with chronic pain prescribed LTOT. In TCM, a lower-intensity intervention, a clinical pharmacist provides care management, structured symptom monitoring, and pain medication optimization. In IPT, a higher-intensity intervention, an interdisciplinary clinician team delivers care using a multi-modal approach to target biopsychosocial contributors to pain and disability with emphasis on non-drug therapies and behavioral activation sessions.

All participants were randomized to receive either IPT or TCM interventions for 12 months. Common elements of both interventions included individualized pain care and opioid taper recommendations tailored to patient preferences and treatment goals. Outcomes were assessed every three months and the primary time point for comparisons was 12 months. The primary outcome was pain, assessed with the Brief Pain Inventory (BPI) total score, with secondary outcomes as opioid daily dose and quality of life measures.