Negative Outcomes of Blepharoplasty and Thyroid Disorders: Is Compensation Always Due? A Case Report with a Literature Review.

BACKGROUND: Plastic surgery is one of the medical specialties with the highest risk of recurrent medical malpractice claims. The frequency of civil lawsuits represents an issue for the micro- and macro-economy of practitioners of these health treatments. This paper aims to discuss the medico-legal aspects and claim path in a case of a cosmetic blepharoplasty complicated by lagophthalmos wrongly related to the procedure but due to missed hyperthyroidism. CASE DESCRIPTION AND LITERATURE REVIEW: A 48-year-old woman who underwent cosmetic blepharoplasty with undiagnosed hyperthyroidism claimed that the lagophthalmos that occurred some months after the procedure was due to medical malpractice, due to an over-resection of the exuberant lower eyelid tissue. The review question was, "Are thyroid disfunctions usually considered contraindications to be communicated to patients who undergo blepharoplasty?", and the databases MEDLINE via PubMed, Embase, Scopus, Ovid, ISI Web of Science, Cochrane, and Google Scholar were used. RESULTS AND DISCUSSION: There were 21 eligible papers. The case highlights the importance and complexity of causal inference (such as unknown thyroid dysfunctions), related informed consent involving information on possible complications unrelated to malpractice, and guidelines recommending endocrinological consultation for cosmetic/functional blepharoplasty in patients at risk (e.g., female patients with a known history of thyroid disease).

Combined Use of Glucose-Specific Model Identification and Alarm Strategy Based on Prediction-Funnel to Improve Online Forecasting of Hypoglycemic Events.

BACKGROUND: Advanced decision support systems for type 1 diabetes (T1D) management often embed prediction modules, which allow T1D people to take preventive actions to avoid critical episodes like hypoglycemia. Real-time prediction of blood glucose (BG) concentration relies on a subject-specific model of glucose-insulin dynamics. Model parameter identification is usually based on the mean square error (MSE) cost function, and the model is usually used to predict BG at a single prediction horizon (PH). Finally, a hypo-alarm is raised if the predicted BG crosses a threshold. This work aims to show that real-time hypoglycemia forecasting can be improved by leveraging: a glucose-specific mean square error (gMSE) cost function in model's parameters identification, and a "prediction-funnel," that is, confidence intervals (CIs) for multiple PHs, within the hypo-alarm-raising strategy. METHODS: Autoregressive integrated moving average with exogenous input (ARIMAX) models are selected to illustrate the proposed solution (use of gMSE and prediction-funnel) and its assessment against the conventional approach (MSE and single PH). The gMSE penalizes the model misfit in unsafe BG ranges (e.g., hypoglycemia), and the prediction-funnel allows raising an alarm by monitoring if the CIs cross a suitable threshold. The algorithms were evaluated by measuring precision (P), recall (R), F1-score (F1), false positive per day (FP/day), and time gain (TG) on a real dataset collected in 11 T1D individuals. RESULTS: The best performance is achieved exploiting both the gMSE and the prediction-funnel: P = 65%, R = 88%, F1 = 75%, FP/day = 0.29, and mean TG = 15 minutes. CONCLUSIONS: The combined use of a glucose-specific metric and an alarm-raising strategy based on the prediction-funnel allows achieving a more effective and reliable hypoglycemia prediction algorithm.

Linear Model Identification for Personalized Prediction and Control in Diabetes.

OBJECTIVE: Type-1 diabetes (T1D) is a disease characterized by impaired blood glucose (BG) regulation, forcing patients to multiple daily therapeutic actions, including insulin administration. T1D management could considerably benefit of accurate BG predictions and automated insulin delivery. For both tasks, the large inter- and intra-individual variability in glucose response represents a major challenge. This work investigates different techniques to learn individualized linear models of glucose response to insulin and meal, suitable for model-based prediction and control. METHODS: We focus on data-driven techniques for linear model-learning and compare the state-of-art parametric pipeline with a novel non-parametric approach based on Gaussian regression and Stable-Spline kernel. On data collected by 11 T1D individuals, the effectiveness of different models was evaluated by measuring root mean squared error (RMSE), coefficient of determination (COD), and time gain associated with BG predictors. RESULTS: Among the tested approaches, the non-parametric technique results in the best prediction performance: median RMSE = 29.8 mg/dL, and median COD = 57.4%, for a prediction horizon (PH) of 60 min. With respect to the state-of-the-art parametric techniques, the non-parametric approach grants a COD improvement of about 2%, 7%, 21%, and 41% for PH = 30, 60, 90, and 120 min (paired-sample t-test p ≤ 0.001, p = 0.003, p = 0.03, and p = 0.07 respectively). CONCLUSION: Non-parametric linear model-learning grants statistically significant improvement with respect to the state-of-art parametric approach. The higher PH, the more pronounced the improvement. SIGNIFICANCE: The use of a linear non-parametric model-learning approach for model-based prediction and control could bring to a more prompt, safe and effective T1D management.

Accuracy of a CGM Sensor in Pediatric Subjects With Type 1 Diabetes. Comparison of Three Insertion Sites: Arm, Abdomen, and Gluteus.

BACKGROUND: Patients with diabetes, especially pediatric ones, sometimes use continuous glucose monitoring (CGM) sensor in different positions from the approved ones. Here we compare the accuracy of Dexcom® G5 CGM sensor in three different sites: abdomen, gluteus (both approved) and arm (off-label). METHOD: Thirty youths, 5-9 years old, with type 1 diabetes (T1D) wore the sensor during a clinical trial where frequent self-monitoring of blood glucose (SMBG) measurements were obtained. Sensor was inserted in different sites according to the patient habit. Accuracy metrics include absolute relative difference (ARD) and absolute difference (AD) of CGM with respect to SMBG. The three sites were compared with ANOVA. If the test detected a difference, an additional pair-wise comparison was performed. RESULTS: Overall, no accuracy difference was detected: the mean ARD was 13.3% (SD = 13.5%) for abdomen, 13.4% (12.9%) for arm and 12.9% (20.2%) for gluteus ( P value = .83); the mean AD was 17.0 mg/dl (17.2 mg/dl) for abdomen, 17.2 mg/dl (17.1 mg/dl) for arm and 18.3 mg/dl (18.5 mg/dl) for gluteus ( P value = .30). In hypo- and euglycemia ARD ( P value = .87 and .15, respectively), and AD ( P value = .68 and .37, respectively) were not statistically different. At variance, in hyperglycemia, a significant difference was detected between the two approved sites, abdomen and gluteus (ΔARD = -2.2% [CI = -4.2%, -0.1%], P value = .04), whereas the comparisons with the off-label location, arm-abdomen, and arm-gluteus were not significant. CONCLUSIONS: These results suggest that the accuracy of the sensor placed on the arm was not significantly different with respect to the two approved insertion sites (abdomen and gluteus). Larger, randomized trials are needed to draw final conclusions.