The Prevalence of Adverse Childhood Experiences, Body Shame, and Revision Request Rate in 218 Plastic Surgery Patients: What Drives Postoperative Dissatisfaction?

BACKGROUND: What drives patients who are unhappy despite good results? Adverse childhood experiences are common, can impair adult health, and can cause body shame. Neither adverse childhood experiences nor body shame has been studied in surgical patients. The authors report adverse childhood experience prevalences in a plastic surgical population and investigate associations from adverse childhood experiences to body shame and to postoperative dissatisfaction. METHODS: Two hundred eighteen consecutive patients (86 percent aesthetic and 14 percent reconstructive) completed the Adverse Childhood Experiences Survey and the Experience of Shame Scale. A one-sample test of proportions, logistic regression, and mediation analysis assessed outcomes. RESULTS: Compared to the Kaiser/Centers for Disease Control and Prevention medical population, our patients had higher overall adverse childhood experience prevalences (79.8 percent versus 64 percent), emotional abuse (41 percent versus 11 percent), emotional neglect (38 percent versus 15 percent), family substance abuse (36 percent versus 27 percent), and family mental illness (29 percent versus 19 percent, all p < 0.001). Fifty-two percent of our patients had body shame. Adverse Childhood Experiences score predicted body shame (OR, 1.22; p = 0.003). Compared to unshamed patients, body shame was associated with more adverse childhood experiences (85 percent versus 72 percent), higher median Adverse Childhood Experiences score (3.5 versus 2), more cosmetic operations (three versus zero), more health problems (three versus two), higher antidepressant use (39 percent versus 19 percent), substance abuse history (16 percent versus 5 percent), and demands for additional pain medication (18 percent versus 5 percent). Body shame predicted requests for surgical revision (49 percent versus 17 percent; OR, 4.61; all p ≤ 0.0001). CONCLUSIONS: Adverse childhood experience were common in our patients. Adverse Childhood Experiences score predicted body shame, which predicted revision requests. If body shame preceded and drove surgery, revision requests were likely. Patients desiring revisions had recognizable characteristics. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.

NATIONAL SURVEY OF ENDOCRINOLOGISTS AND SURGEONS REGARDING ACTIVE SURVEILLANCE FOR LOW-RISK PAPILLARY THYROID CANCER.

OBJECTIVE: Active surveillance for low-risk papillary thyroid cancer (PTC) was endorsed by the American Thyroid Association guidelines in 2015. The attitudes and beliefs of physicians treating thyroid cancer regarding the active surveillance approach are not known. METHODS: A national survey of endocrinologists and surgeons treating thyroid cancer was conducted from August to September 2017 via professional society emails. This mixed-methods analysis reported attitudes toward potential factors impacting decision-making regarding active surveillance, beliefs about barriers and facilitators of its use, and reasons why physicians would pick a given management strategy for themselves if they were diagnosed with a low-risk PTC. Survey items about attitudes and beliefs were derived from the Cabana model of barriers to guideline adherence and theoretical domains framework of behavior change. RESULTS: Among 345 respondents, 324 (94%) agreed that active surveillance was appropriate for at least some patients, 81% agreed that active surveillance was at least somewhat underused, and 76% said that they would choose surgery for themselves if diagnosed with a PTC of ≤1 cm. Majority of the respondents believed that the guidelines supporting active surveillance were too vague and that the current supporting evidence was too weak. Malpractice and financial concerns were identified as additional barriers to offering active surveillance. The respondents endorsed improved information resources and evidence as possible facilitators to offering active surveillance. CONCLUSION: Although there is general support among physicians who treat low-risk PTC for the active surveillance approach, there is reluctance to offer it because of the lack of robust evidence, guidelines, and protocols.

Factors associated with physicians' recommendations for managing low-risk papillary thyroid cancer.

BACKGROUND: The 2015 American Thyroid Association endorsed less aggressive management for low-risk papillary thyroid cancer (LR-PTC). We aimed to identify factors influencing physicians' recommendations for LR-PTC. METHODS: We surveyed members of three professional societies and assessed respondents' recommendations for managing LR-PTC using patient scenarios. Multivariable logistic regression models identified clinical and non-clinical factors associated with recommending total thyroidectomy (TT) and active surveillance (AS). RESULTS: The 345 respondents included 246 surgeons and 99 endocrinologists. Physicians' preference for their own management if diagnosed with LR-PTC had the strongest association with their recommendation for TT and AS (TT: OR 12.3; AS: OR 7.5, p < 0.001). Physician specialty and stated patient preference were also significantly associated with their recommendations for both management options. Respondents who received information about AS had increased odds of recommending AS. CONCLUSIONS: Physicians' recommendations for LR-PTC are strongly influenced by non-clinical factors, such as personal treatment preference and specialty.

Optimizing Levothyroxine Dose Adjustment After Thyroidectomy With a Decision Tree.

BACKGROUND: After thyroidectomy, patients require Levothyroxine (LT4). It may take years of dose adjustments to achieve euthyroidism. During this time, patients encounter undesirable symptoms associated with hypo- or hyper-thyroidism. Currently, providers adjust LT4 dose by clinical estimation, and no algorithm exists. The objective of this study was to build a decision tree that could estimate LT4 dose adjustments and reduce the time to euthyroidism. METHODS: We performed a retrospective cohort analysis on 320 patients who underwent total or completion thyroidectomy at our institution. All patients required one or more LT4 dose adjustments from their initial postoperative dose before attaining euthyroidism. Using the Classification and Regression Tree algorithm, we built various decision trees from patient characteristics, estimating the dose adjustment to reach euthyroidism. RESULTS: The most accurate decision tree used thyroid-stimulating hormone values at first dose adjustment (mean absolute error = 13.0 µg). In comparison, the expert provider and naïve system had a mean absolute error of 11.7 µg and 17.2 µg, respectively. In the evaluation dataset, the decision tree correctly predicted the dose adjustment within the smallest LT4 dose increment (12.5 µg) 79 of 106 times (75%, confidence interval = 65%-82%). In comparison, expert provider estimation correctly predicted the dose adjustment 76 of 106 times (72%, confidence interval = 62%-80%). CONCLUSIONS: A decision tree predicts the correct LT4 dose adjustment with an accuracy exceeding that of a completely naïve system and comparable to that of an expert provider. It can assist providers inexperienced with LT4 dose adjustment.

Identifying Predictors of Prolonged Levothyroxine Dose Adjustment After Thyroidectomy.

BACKGROUND: Levothyroxine (LT4) is one of the most prescribed drugs in the United States; however, many patients started on LT4 after thyroidectomy suffer from symptoms of hyper- or hypo-thyroidism before achieving euthyroidism. This study aims to describe the time required for dose adjustment before achieving euthyroidism and identify predictors of prolonged dose adjustment (PDA+) after thyroidectomy. METHODS: This is a single institution retrospective cohort study of patients who achieved euthyroidism with LT4 therapy between 2008 and 2017 after total or completion thyroidectomy for benign disease. Patients who needed at least three dose adjustments (top quartile) were considered PDA+. Binomial logistic regression was used to identify predictors of PDA+. RESULTS: The 605 patients in this study achieved euthyroidism in a median of 116 d (standard deviation 124.9) and one dose adjustment (standard deviation 1.3). The 508 PDA- patients achieved euthyroidism in a median of 101 d and one dose adjustment. The 97 PDA+ patients achieved euthyroidism in a median of 271 d and three dose adjustments. Iron supplementation (odds ratio = 4.4, 95% confidence interval = 1.4-13.5, P = 0.010) and multivitamin with mineral supplementation (odds ratio = 2.4, 95% confidence interval = 1.3-4.3, P = 0.004) were independently associated with PDA+. Age, gender, preoperative thyroid disease, and comorbidities did not independently predict PDA+. CONCLUSIONS: After thyroidectomy, achieving euthyroidism can take nearly 4 mo. Iron and mineral supplementation are associated with PDA+. This information can inform the preoperative counseling of patients and suggests that this may expedite achieving euthyroidism.

Enteric conversion after bladder-drained pancreas transplantation is not associated with worse allograft survival.

In the early experience of pancreas transplantation, bladder drainage was favored, but it often caused urologic, metabolic, and infectious complications that necessitated conversion to enteric drainage. Long-term graft survival after enteric conversion and the impact of time interval from transplantation to enteric conversion on graft survival is poorly understood. We studied all bladder-drained first-time pancreas transplantations performed at the University of Wisconsin from 1985 to 2000. Time to conversion was estimated with the Kaplan-Meier technique, whereas risk factors associated with conversion were estimated via a time-varying Cox proportional hazards model. Of 386 bladder-drained pancreata, 162 (41.9%) eventually required enteric conversion, 29 (17.9%) within the first year. Median time to conversion varied by indication: 0.68 years for surgical, 3.1 years for urologic, and 2.7 years for metabolic disorders. In a time-varying Cox model adjusting for donor and recipient factors, enteric conversion did not affect the risk of pancreas graft loss (hazard ratio [HR] 0.86, P = .26). Kidney survival was not associated with enteric conversion. When necessary due to symptoms or complications, enteric conversion of bladder-drained pancreata is safe and does not affect overall graft survival. This relationship appears to be true no matter when the conversion is performed.

The optimal dosing scheme for levothyroxine after thyroidectomy: A comprehensive comparison and evaluation.

BACKGROUND: Patients often struggle to attain euthyroidism after thyroidectomy, and multiple dosing schemes have been proposed to supplant the standard weight-based approach for initial levothyroxine dosing after thyroidectomy. The objectives of this study were to review the literature for existing levothyroxine dosing schemes and compare estimation accuracies with novel schemes developed with machine learning. METHODS: This study retrospectively analyzed 598 patients who attained euthyroidism after total or completion thyroidectomy for benign disease. A scoping review identified existing levothyroxine dosing schemes. Thirteen machine learning algorithms estimated euthyroid dose. Using 10-fold cross-validation, we compared schemes by the proportion of patients having a predicted dose within 12.5 µg/day of their euthyroid dose. RESULTS: Of 264 reviewed articles, 7 articles proposed retrospectively implementable dosing schemes. A novel Poisson regression model proved most accurate, correctly predicting 64.8% of doses. Incorporating 7 variables, Poisson regression was significantly more accurate than the best scheme in the literature (body mass index/weight based) that correctly predicted 60.9% of doses (P = .031). Standard weight-based dosing (1.6 µg/kg/day) correctly predicted 51.3% of doses, and the least effective scheme (age/sex/weight based) correctly predicted 27.4% of doses. CONCLUSION: Using readily available variables, a novel Poisson regression dosing scheme outperforms other machine learning algorithms and all existing schemes in estimating levothyroxine dose.