Patient Characteristics Associated With Self-Reported Adherence to Chiropractic Treatment Recommendations: A Feasibility Study

2021 ◽  
Author(s):  
Serena Bezdjian ◽  
James M. Whedon ◽  
Robb Russell ◽  
Ian Coulter
Keyword(s):  
Feasibility Study ◽  
Patient Characteristics ◽  
Treatment Recommendations ◽  
Chiropractic Treatment

Initiating anticoagulant therapy after ICH is associated with patient characteristics and treatment recommendations

2018 ◽  
Vol 265 (10) ◽  
pp. 2404-2414 ◽  
Author(s):  
Jochen A. Sembill ◽  
Claudia Y. Wieser ◽  
Maximilian I. Sprügel ◽  
Stefan T. Gerner ◽  
Antje Giede-Jeppe ◽  
...  
Keyword(s):  
Anticoagulant Therapy ◽  
Patient Characteristics ◽  
Treatment Recommendations

496 NON-RANDOMISED FEASIBILITY STUDY OF THE REHABILITATION POTENTIAL ASSESSMENT TOOL (REPAT) IN FRAIL OLDER PEOPLE IN ACUTE SETTING

2021 ◽  
Vol 50 (Supplement_2) ◽  
pp. ii5-ii7
Author(s):  
A Cowley ◽  
S E Goldberg ◽  
A L Gordon ◽  
P A Logan
Keyword(s):  
Older People ◽  
Feasibility Study ◽  
Assessment Tool ◽  
Occupational Therapists ◽  
Patient Characteristics ◽  
Structured Interviews ◽  
Clinical Frailty Scale ◽  
Mean Values ◽  
Rehabilitation Potential ◽  
Acute Hospitals

Abstract Introduction Clinicians are often required to decide about patients’ potential to respond to rehabilitation. ‘Rehabilitation potential’ can determine what services patients can access. In acute hospitals clinicians have limited time to assess and deliver rehabilitation which takes into account the complexities of frailty and ageing. We set out to evaluate whether the Rehabilitation Potential Assessment Tool (RePAT)—a 15 item assessment tool and training package which emphasises person-centred approaches—was feasible and could aid rehabilitation decisions. Method A non-randomised feasibility study with nested semi-structured interviews, set in the acute hospital, explored whether RePAT was deliverable and acceptable to staff, patient and carers. A maximum variation sample of physiotherapists and occupational therapists was recruited. Patient and carer participants were recruited from Healthcare of Older People wards. Staff and patient characteristics were summarised using descriptive statistics. Interview data were analysed thematically. Fidelity of completed RePAT items was assessed on how closely they matched tool guidance by two reviewers. Mean values of the two scores were calculated. Results Six staff participants were recruited and trained, and assessed a total of 26 patient participants using RePAT. Mean patient age was 86.16 (±6.39) years. 32% were vulnerable or mildly frail, 42% moderately frail and 26% severely or very severely frail using the Clinical Frailty Scale. Mean time to complete RePAT was 32.7 (±9.6) minutes. 13 out of 15 RePAT items achieved fidelity. RePAT was acceptable and tolerated by staff and patients. Staff participants reported RePAT enabled them to consider the complex and dynamic nature of rehabilitation decisions in a more structured and consistent way. Conclusion RePAT was found to be acceptable and tolerated by staff, carers and patients. It allowed clinicians to make explicit their reasoning behind rehabilitation potential decision-making and encouraged them to become more cognisant of ethical dilemmas and biases.

scholarly journals Estimating and evaluating personalized treatment recommendations from randomized trials with ptr

2021 ◽  
Vol 21 (2) ◽  
pp. 348-359
Author(s):  
Matthias Pierce ◽  
Richard Emsley
Keyword(s):  
Randomized Trials ◽  
Patient Characteristics ◽  
Treatment Recommendations ◽  
Trial Data ◽  
Personalized Treatment ◽  
Treatment Recommendation ◽  
Bootstrap Resampling ◽  
Recommendation Algorithm ◽  
Continuous Outcome ◽  
Alternative Regime

One of the targets of personalized medicine is to provide treatment recommendations using patient characteristics. We present the command ptr, which both predicts a personalized treatment recommendation algorithm and evaluates its effectiveness versus an alternative regime, using randomized trial data. The command allows for multiple (continuous or categorical) biomarkers and a binary or continuous outcome. Confidence intervals for the evaluation parameter are provided using bootstrap resampling.

scholarly journals 0604 Comorbidities and Admission Rates in Inpatients Undergoing Sleep Studies

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A231-A231
Author(s):  
K G Johnson ◽  
N Ravikumar ◽  
N Scuderi ◽  
A Sharma ◽  
V Rastegar ◽  
...  
Keyword(s):  
Average Length ◽  
Medical Center ◽  
Central Sleep Apnea ◽  
Patient Characteristics ◽  
Median Number ◽  
Treatment Recommendations ◽  
Subsequent Treatment ◽  
Sleep Study ◽  
Sleep Studies ◽  
Obesity Hypoventilation

Abstract Introduction Uncontrolled sleep-disordered breathing (SDB) and hypoventilation, which are common in COPD, CHF and obesity hypoventilation patients can lead to death and readmissions. It is unknown whether inpatient sleep studies to diagnose and optimize treatment improve care and prevent readmissions. Methods All patients > 18 years old with sleep studies while inpatient at Baystate Medical Center between October 2015 and September 2017 were included. Patient characteristics, comorbidities, sleep study diagnoses, and treatment recommendations were evaluated. Admission (inpatient or observation) and death rates were determined for 1-year before admit date and 1-year after discharge date of index admission. Results 326 adult inpatients had 120 portable and 304 in-laboratory tests performed. Average age was 62.9±14.4, mean BMI was 37.2±12.3 and 56% were male. Principal diagnoses were CHF (50%), COPD (39%), both COPD and CHF (20%) and obesity hypoventilation (27%). 31 used PAP and 71 used oxygen prior to admission. Sleep diagnoses included OSA (73%), central sleep apnea (CSA) (29%), treatment emergent CSA (8%), hypoxia (48%), hypoventilation (41%), and normal or non-diagnostic (6%). Treatment recommendations included CPAP (25%), BiPAP (18%), BiPAP ST (3%), ASV (4%), iVAPS (22%), oxygen only (5%) and further titration (20%). The average length of stay was 11.6 ± 9.6 days. There was no difference in the percentage of patients who had an admission before or after their sleep study (53% vs 56%, respectively). In addition, no difference was seen in the median number of admissions before and after the sleep study (median=1.0, IQI=0-2, p=0.77). 90-day readmission rate was 19%. 14% died. Conclusion SDB, hypoxia and hypoventilation were common in inpatients evaluated with sleep studies with PAP therapy recommended in most patients. Further research is needed to determine whether inpatient testing and subsequent treatment can result in decreased readmissions and death. Support None

scholarly journals A demonstration of a multi-method variable selection approach for treatment selection: Recommending cognitive-behavioral versus psychodynamic therapy for mild to moderate adult depression

2017 ◽  
Author(s):  
Zachary Daniel Cohen ◽  
Thomas Kim ◽  
Henricus Van ◽  
Jack Dekker ◽  
Ellen Driessen
Keyword(s):  
Variable Selection ◽  
Rating Scale ◽  
Psychological Treatment ◽  
Selection Procedure ◽  
Patient Characteristics ◽  
Treatment Recommendations ◽  
Psychodynamic Therapy ◽  
Treatment Selection ◽  
Cognitive Behavioral ◽  
Treatment Needs

Objective: We use a new variable selection procedure for treatment selection which generates treatment recommendations based on pre-treatment characteristics for adults with mild-to-moderate depression deciding between cognitive behavioral (CBT) versus psychodynamic therapy (PDT).Method: Data are drawn from a randomized comparison of CBT versus PDT for depression (N=167, 71%-female, mean-age=39.6). The approach combines four different statistical techniques to identify patient characteristics associated consistently with differential treatment response. Variables are combined to generate predictions indicating each individual’s optimal-treatment. The average outcomes for patients who received their indicated treatment versus those who did not were compared retrospectively to estimate model utility.Results: Of 49 predictors examined, depression severity, anxiety sensitivity, extraversion, and psychological treatment-needs were included in the final model. The average post-treatment Hamilton-Depression-Rating-Scale score was 1.6 points lower (95%CI=[0.5:2.8]; d=0.21) for those who received their indicated-treatment compared to non-indicated. Among the 60% of patients with the strongest treatment recommendations, that advantage grew to 2.6 (95%CI=[1.4:3.7]; d=0.37). Conclusions: Variable selection procedures differ in their characterization of the importance of predictive variables. Attending to consistently-indicated predictors may be sensible when constructing treatment selection models. The small-N and lack of separate validation sample indicate a need for prospective tests before this model is used.

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