Background Differences in patient–physician interactions based on physician gender have been demonstrated. However, the association between patients' self‐perceived health and their decision to see a female versus male physician is still unclear. Objective To determine if self‐reported physical or behavioral health is different in musculoskeletal patients who present to female vs male physicians. We hypothesized that patients who present to female physicians report worse physical and behavioral health. Design Cross‐sectional study. Setting Tertiary academic medical center. Patients Consecutive 21 980 adult patients who presented to a musculoskeletal medicine specialist for initial evaluation of a musculoskeletal condition between April 1, 2016 and November 1, 2017. Main Outcome Measures Physical Function, Pain Interference, Anxiety, and Depression Computer Adaptive Test domains of the Patient‐Reported Outcomes Measurement Information System (PROMIS). The primary study outcome was the mean difference (MD) in PROMIS scores by physician gender. Results Patients who presented to female physicians self‐reported slightly worse health in all domains: Physical Function (female physicians 40.2, male physicians 42.4, MD ‐2.1; 95% confidence interval [CI] ‐2.5 to −1.8), Pain Interference (female physicians 61.6, male physicians 60.4, MD 1.3 [1.0‐1.5]), Anxiety (female physicians 52.5, male physicians 51.4, MD 1.1 [0.8‐1.5]), and Depression (female physicians 47.5, male physicians 46.2, MD 1.3 [0.9‐1.6]) (all P < .001). Patients who presented to female physicians were also slightly younger (51.9 vs 52.4 years, P = .034) and more likely to be female (63% vs 56%, P < .001). Conclusions Patients who presented to female physicians self‐reported slightly worse physical and behavioral health compared to those patients who presented to male physicians. Further investigation into this finding may provide insight into drivers of patients' preferences, which may enable physicians of both genders to optimize patient care.
Abstract Objective: Assess response to intravenous immunoglobulin (IVIG) in presumed autoimmune postural orthostatic tachycardia syndrome (POTS) Background: POTS predominantly affects young women and may be associated with systemic autoimmune disorders, serum autoantibodies or recent infection. Uncontrolled case studies suggest that IVIG is beneficial for treating autoimmune POTS. However, no previous randomized controlled trials have been conducted. Methods: This single site randomized controlled clinical trial compared IVIG to intravenous albumin infusions. Albumin comparator ensured blinding and control for effects of volume expansion. Eligible POTS patients had COMPASS-31 score ≥ 40 and met pre-determined criteria suggesting autoimmunity. Over 12 weeks, participants received 8 infusions (0.4gm/kg each). Four infusions were given weekly followed by four infusions every other week. Primary outcome measure was improvement in COMPASS-31 two weeks after final infusion. Results: 50 participants consented; 30 met inclusion criteria and received study drug (16 IVIG and 14 albumin; 29 female). Group baseline characteristics were well matched. 27 participants completed treatment protocol. Change in COMPASS-31 did not differ between groups (median change [IQR]; IVIG: -5.5 [-23.3, 2.5] vs. Albumin: -10.6 [-14.1, -4.7]; p-value = 0.629). Response rate was also not different between groups. Adverse events were common but usually mild and did not differ between treatment groups. Conclusions: This first randomized controlled trial of IVIG in POTS found no difference in symptom response compared to albumin infusion. Both groups showed improvement possibly related to volume expansion obscuring other benefits. Future clinical trials may benefit from the use of POTS-specific clinical outcome measures sensitive to symptoms other than orthostatic intolerance.
We tested for the presence of differential item functioning (DIF) in commonly used measures of depressive symptoms, in people with multiple sclerosis (MS) versus people with a psychiatric disorder without MS.Participants included individuals with MS, or with a lifetime history of a depressive or anxiety disorder (Dep/Anx) but no immune-mediated inflammatory disease. Participants completed the Patient Health Questionnaire (PHQ-9), Hospital Anxiety and Depression Scale (HADS), and the Patient Reported Outcome Measurement Information System (PROMIS)-Depression. We assessed unidimensionality of the measures using factor analysis. We evaluated DIF using logistic regression, with and without adjustment for age, gender and body mass index (BMI).We included 555 participants (MS: 252, Dep/Anx: 303). Factor analysis showed that each depression symptom measure had acceptable evidence of unidimensionality. In unadjusted analyses comparing the MS versus Dep/Anx groups we identified multiple items with evidence of DIF, but few items showed DIF effects that were large enough to be clinically meaningful. We observed non-uniform DIF for one PHQ-9 item, and three HADS-D items. We also observed DIF with respect to gender (one HADS-D item), and BMI (one PHQ-9 item). For the MS versus Dep/Anx groups, we no longer observed DIF post-adjustment for age, gender and BMI. On unadjusted and adjusted analyses, we did not observe DIF for any PROMIS-D item.Our findings suggest that DIF exists for the PHQ-9 and HADS-D with respect to gender and BMI in clinical samples that include people with MS whereas DIF was not observed for the PROMIS-Depression scale.
Background: Researchers studying health-related quality of life (HRQOL) in multiple sclerosis (MS) can choose from many instruments, but findings from studies which use different instruments cannot be easily combined. We aimed to develop a crosswalk that associates scores from the RAND-12 to scores on the Health Utilities Index—Mark III (HUI3) in persons with MS. Methods: In 2018, participants in the North American Research Committee on Multiple Sclerosis (NARCOMS) registry completed the RAND-12 and the HUI3 to assess HRQOL. We used item-response theory (IRT) and equipercentile linking approaches to develop a crosswalk between instruments. We compared predicted scores for the HUI3 from each crosswalk to observed scores using Pearson correlations, intraclass correlation coefficients (ICCs), and Bland–Altman plots. Results: Of 11,389 invited participants, 7129 (62.6%) responded. Predicted and observed values of the HUI3 from the IRT-linking method were moderately correlated (Pearson r = 0.76) with good concordance (ICC = 0.72). However, the Bland–Altman plots suggested biased prediction. Predicted and observed values from the equipercentile linking method were also moderately correlated (Pearson r = 0.78, ICC = 0.78). The Bland–Altman plots suggested no bias. Conclusion: We developed a crosswalk between the RAND-12 and the HUI3 in the MS population which will facilitate data harmonization efforts.
BackgroundRadiomics analyses has been proposed to interrogate the biology of tumour as well as to predict/assess response to therapy in vivo. The objective of this work was to assess the sensitivity of radiomics features to noise, resolution, and tumour volume in the context of a co-clinical trial.MethodsTriple negative breast cancer (TNBC) patients were recruited into an ongoing co-clinical imaging trial. Sub-typed matched TNBC patient-derived tumour xenografts (PDX) were generated to investigate optimal co-clinical MR radiomic features. The MR imaging protocol included T1-weighed and T2-weighted imaging. To test the sensitivity of radiomics to resolution, PDX were imaged at three different resolutions. Multiple sets of images with varying signal-to-noise ratio (SNR) were generated, and an image independent patch-based method was implemented to measure the noise levels. Forty-eight radiomic features were extracted from manually segmented 2D and 3D segmented tumours and normal tissues of T1- and T2- weighted co-clinical MR images.FindingsSixteen radiomics features were identified as volume dependent and corrected for volume-dependency following normalization. Features from grey-level run-length matrix (GLRLM), grey-level size zone matrix (GLSZM) were identified as most sensitive to noise. Radiomic features Kurtosis and Run-length variance (RLV) from GLSZM were most sensitive to changes in resolution in both T1w and T2w MRI. In general, 3D radiomic features were more robust compared to 2D (single slice) measures, although the former exhibited higher variability between subjects.InterpretationTumour volume, noise characteristics, and image resolution significantly impact radiomic analysis in co-clinical studies.
The Liver Imaging Reporting and Data System (LI-RADS) was created to standardize the diagnostic criteria for hepatocellular carcinoma (HCC) and has undergone multiple revisions including a recent update in 2018 (v2018). The primary aim of this study was to determine the diagnostic performance and interrater reliability (IRR) of LI-RADS v2018 for distinguishing HCC from non-HCC primary hepatic malignancy in patients 'at-risk' for HCC. A secondary aim was to assess the impact of changes introduced in the v2018 diagnostic algorithm.This retrospective study combined a 10-year experience of pathologically proven primary liver malignancies from two large liver transplant centers. Two blinded readers independently evaluated each lesion and assigned a LI-RADS diagnostic category, additionally scoring all relevant imaging features. Changes in category based on the reader-provided features and the new v2018 criteria were assessed by a study coordinator.The final study cohort comprised 105 HCCs and 73 non-HCC primarily liver malignancies. LI-RADS had a high specificity for distinguishing HCC from non-HCC (89% and 90% for reader 1 and reader 2, respectively), and IRR was moderate to substantial for final LI-RADS category and most features. Revision of the LI-RADS v2018 diagnostic algorithm resulted in very few changes [5 (2.8%) and 3 (1.7%) for reader 1 and reader 2, respectively] in overall lesion classification.LI-RADS diagnostic categories and features had moderate to substantial IRR and high specificity for distinguishing HCC from non-HCC primary liver malignancy. Revision of LI-RADS v2018 diagnostic algorithm resulted in reclassification of very few lesions.
