Recent Research Publications- February 2023
Sutcliffe S, Newcomb C, Bradley CS, Clemens JQ, Erickson B, Gupta P, Lai HH, Naliboff B, Strachan E, Stephens-Shields A.
J Urol. 2023 Jan 11:101097JU0000000000003155. doi: 10.1097/JU.0000000000003155. Epub ahead of print. PMID: 36630590.
Purpose: Most studies on interstitial cystitis/bladder pain syndrome and chronic prostatitis/chronic pelvic pain syndrome use typical or average levels of pelvic pain or urological symptom intensity as their outcome, as both are associated with reduced quality of life. Symptom exacerbations or "flares" have also been found to be associated with reduced quality of life, but no studies, to our knowledge, have investigated whether these associations are independent of typical pelvic pain levels and thus might be useful additional outcome measures (or stated differently, whether reducing flare frequency even without reducing mean pain intensity may be important to patients).
Materials and methods: We used screening visit and weekly run-in period data from the Multidisciplinary Approach to the Study of Chronic Pelvic Pain Symptom Patterns Study to investigate associations between flare frequency and multiple measures of illness impact and health care seeking activity, independent of typical nonflare and overall pelvic pain levels.
Results: Among the 613 eligible participants, greater flare frequency was associated with worse condition-specific illness impact (standardized β coefficients=0.11-0.68, P trends < .0001) and health care seeking activity (odds ratios=1.52-3.94, P trends .0039 to < .0001) in analyses adjusted for typical nonflare and overall pelvic pain levels. Experiencing ≥1/d was also independently associated with worse general illness impact (standardized β coefficients=0.11-0.25).
Conclusions: Our findings suggest that flare frequency and possibly other flare characteristics may be worth considering as additional outcome measures in urological chronic pelvic pain syndrome research to support the development of new preventive and therapeutic flare strategies.
Circulating microparticle proteins predict pregnancies complicated by placenta accreta spectrum.
Yu HY, Gumusoglu SB, Cantonwine DE, Carusi DA, Gurnani P, Schickling B, Doss RC, Santillan MK, Rosenblatt KP, McElrath TF.
Sci Rep. 2023 Jan 5;12(1):21922. doi: 10.1038/s41598-022-24869-0. PMID: 36604494; PMCID: PMC9814521.
Placenta accreta spectrum (PAS) is characterized by abnormal attachment of the placenta to the uterus, and attempts at placental delivery can lead to catastrophic maternal hemorrhage and death. Multidisciplinary delivery planning can significantly improve outcomes; however, current diagnostics are lacking as approximately half of pregnancies with PAS are undiagnosed prior to delivery. This is a nested case-control study of 35 cases and 70 controls with the primary objective of identifying circulating microparticle (CMP) protein panels that identify pregnancies complicated by PAS. Size exclusion chromatography and liquid chromatography with tandem mass spectrometry were used for CMP protein isolation and identification, respectively. A two-step iterative workflow was used to establish putative panels. Using plasma sampled at a median of 26 weeks' gestation, five CMP proteins distinguished PAS from controls with a mean area under the curve (AUC) of 0.83. For a separate sample taken at a median of 35 weeks' gestation, the mean AUC was 0.78. In the second trimester, canonical pathway analyses demonstrate over-representation of processes related to iron homeostasis and erythropoietin signaling. In the third trimester, these analyses revealed abnormal immune function. CMP proteins classify PAS well prior to delivery and have potential to significantly reduce maternal morbidity and mortality.
Iwamoto A, Summers KM, Mancuso AC.
J Assist Reprod Genet. 2023 Jan 5. doi: 10.1007/s10815-022-02687-7. Epub ahead of print. PMID: 36602655.
Purpose: To assess public support for insurance coverage of infertility treatment among marginalized patient groups.
Methods: A cross-sectional, web survey-based study using a national sample of 1226 US adults. Participants responded to questions measuring their beliefs and attitudes towards support for infertility treatment insurance coverage among specific patient populations. We then evaluated the opinions of only the participants who supported infertility treatment insurance coverage for patients meeting the standard definition of infertility. Associations between demographic data of participants and support for infertility treatment insurance coverage among these marginalized groups were queried.
Results: Of the total responses, 61.9% of the respondents generally supported insurance coverage for infertility. Of the total responses, 54.5% did not support any insurance coverage for lesbian, gay, or transgender patients. Of those who generally supported the insurance coverage for infertility, 53.0% supported coverage for gay patients requiring infertility services, 54.6% supported coverage for lesbian patients, and 42.5% supported coverage for transgender patients. Of the total responses, 47.6% did not support insurance for green card holders, undocumented immigrants, or refugees. Of those who supported the insurance coverage for infertility in general, 63.6% supported insurance coverage for patients with green cards, 29.8% for refugees, and 20.7% for undocumented patients. For disability and genetic conditions, 39.5% did not support coverage for any groups. Of those who support the insurance coverage for infertility in general, there was most support for patients with physical disabilities (60.2%) followed by genetic disease (47.9%), then mental disabilities (31.4%).
Conclusion: Even among those who support insurance coverage for infertility in general, approximately less than half of them supported these same treatments for marginalized groups, including the diverse sexuality and gender (DSG), immigrant, and disabled populations. Increased education and awareness of infertility is needed among the general population to garner acceptance of infertility as a disease and support insurance coverage of infertility treatment for all persons.
Bartlett CW, Bossenbroek J, Ueyama Y, McCallinhart P, Peters OA, Santillan DA, Santillan MK, Trask AJ, Ray WC.
SN Comput Sci. 2023;4(2):161. doi: 10.1007/s42979-022-01553-8. Epub 2023 Jan 12. PMID: 36647373; PMCID: PMC9836982.
Early stopping is an extremely common tool to minimize overfitting, which would otherwise be a cause of poor generalization of the model to novel data. However, early stopping is a heuristic that, while effective, primarily relies on ad hoc parameters and metrics. Optimizing when to stop remains a challenge. In this paper, we suggest that for some biomedical applications, a natural dichotomy of invasive/non-invasive measurements, or more generally proximal vs distal measurements of a biological system can be exploited to provide objective advice on early stopping. We discuss the conditions where invasive measurements of a biological process should provide better predictions than non-invasive measurements, or at best offer parity. Hence, if data from an invasive measurement are available locally, or from the literature, that information can be leveraged to know with high certainty whether a model of non-invasive data is overfitted. We present paired invasive/non-invasive cardiac and coronary artery measurements from two mouse strains, one of which spontaneously develops type 2 diabetes, posed as a classification problem. Examination of the various stopping rules shows that generalization is reduced with more training epochs and commonly applied stopping rules give widely different generalization error estimates. The use of an empirically derived training ceiling is demonstrated to be helpful as added information to leverage early stopping in order to reduce overfitting.