BackgroundSafe and effective diabetes management in the hospital is challenging. Inadequate knowledge has been identified by trainees as a key barrier. In this study we assess both the short-term and long-term impact of an interactive seminar on medical student knowledge and comfort with hospital diabetes management.MethodsAn interactive seminar covering hospital diabetes management and utilizing an audience response system was added to the third-year medical student curriculum. Students were given a multiple choice assessment immediately before and after the seminar to assess their comprehension of the material. Students were also asked to rate their confidence on this topic. Approximately 6 months later, students were given the same assessment to determine if the improvements in hospital diabetes knowledge and confidence were durable over time. Students from the preceding medical school class, who did not have a hospital diabetes seminar as a part of their curriculum, were used as a control.ResultsFifty–three students participated in the short-term assessment immediately before and after the seminar. The mean score (maximum 15) was 7.7 +/- 2.7 (51%) on the pre-test and 11.4 +/- 1.8 (76%) on the post-test (p < 0.01). 75 students who attended the seminar completed the same set of questions 6 months later with mean score of 9.2 ± 2.3 (61%). The control group of 100 students who did not attend seminar had a mean score of 8.8 ± 2.5 (58%). The difference in scores between the students 6-months after the seminar and the control group was not significantly different (p = 0.30).ConclusionsDespite initial short-term gains, a single seminar on hospital diabetes management did not durably improve trainee knowledge or confidence. Addition of repeated and focused interactions during clinical rotations or other sustained methods of exposure need to be evaluated.Electronic supplementary materialThe online version of this article (doi:10.1186/s40842-016-0038-4) contains supplementary material, which is available to authorized users.
Objective
To predict intra‐operative (IOEs) and postoperative events (POEs) consequential to the derailment of the ideal clinical course of patient recovery.
Materials and Methods
The Vattikuti Collective Quality Initiative is a multi‐institutional dataset of patients who underwent robot‐assisted partial nephectomy for kidney tumours. Machine‐learning (ML) models were constructed to predict IOEs and POEs using logistic regression, random forest and neural networks. The models to predict IOEs used patient demographics and preoperative data. In addition to these, intra‐operative data were used to predict POEs. Performance on the test dataset was assessed using area under the receiver‐operating characteristic curve (AUC‐ROC) and area under the precision‐recall curve (PR‐AUC).
Results
The rates of IOEs and POEs were 5.62% and 20.98%, respectively. Models for predicting IOEs were constructed using data from 1690 patients and 38 variables; the best model had an AUC‐ROC of 0.858 (95% confidence interval [CI] 0.762, 0.936) and a PR‐AUC of 0.590 (95% CI 0.400, 0.759). Models for predicting POEs were trained using data from 1406 patients and 59 variables; the best model had an AUC‐ROC of 0.875 (95% CI 0.834, 0.913) and a PR‐AUC 0.706 (95% CI, 0.610, 0.790).
Conclusions
The performance of the ML models in the present study was encouraging. Further validation in a multi‐institutional clinical setting with larger datasets would be necessary to establish their clinical value. ML models can be used to predict significant events during and after surgery with good accuracy, paving the way for application in clinical practice to predict and intervene at an opportune time to avert complications and improve patient outcomes.
ObjectiveTo evaluate the prevalence and persistence of postoperative glycemic abnormalities in patients without a history of diabetes, undergoing cardiac surgery (CS).MethodsNinety-two patients without diabetes with planned elective CS procedures at a tertiary institution were evaluated preoperatively and 3 months postoperatively for measures of glucose control including hemoglobin A1c, fasting plasma glucose, 2-h post oral glucose load, and insulin levels. Data from the hospital course were recorded.ResultsValid data were available from 61 participants at 3 months; 59% had prediabetes and 10% had diabetes preoperatively by one or more diagnostic criteria and continued to be dysglycemic at 3 months. Preoperative A1C was an independent predictor of postoperative hyperglycemia (p = 0.02). Insulin resistance and BMI rather than glycemic abnormalities before surgery were associated with a longer duration of the postoperative insulin infusion (p = 0.004, p = 0.048).ConclusionSeventy percent of CS patients without known diabetes met criteria for diabetes or prediabetes preoperatively, and these abnormalities persisted after surgery.
In patients before cardiac surgery, A1C criteria identified the largest number of patients with diabetes and prediabetes. For diagnosing prediabetes, A1C and FPG were discordant and characterized different groups of patients, therefore altering the distribution of diabetes risk. Simultaneous measurement of FGP and A1C may be a more sensitive and specific tool for identifying high-risk individuals with diabetes and prediabetes.
Background Deep sternal wound infections (DSWI) after cardiac surgery are a major cause of morbidity and mortality, especially in patients with diabetes mellitus (DM). Although data of postoperative blood glucose (BG) control on surgical outcomes is well established, the impact of a high A1C on morbidity and mortality is still unclear.Objective The purpose of this study was to evaluate the association between preoperative glucose control, as measured by A1C with postoperative outcomes especially DSWI, in cardiac surgery patients with known DM whose postoperative BG was controlled to a goal of 100-140 mg/dl.Methods This is a single-center, retrospective observational study of DM patients who were stratified according to their preoperative A1C: good glycemic control (A1C < 7.0%), moderate glycemic control (A1C 7.0-8.5%), and poor glycemic control (A1C > 8.5%). Postoperative glycemic management was standardized. Cox regression model was used to determine whether A1C was an independent risk factor of DSWI.
ResultsIn 861 diabetes patients with similar postoperative BG control after cardiac surgery, the total incidence of DSWI was 2.8%. Six hundred and sixteen qualified and were stratified by A1C. DSWI rates were 2.3% in good glycemic control, 4.3% in moderate glycemic control, and 8.1% in poor glycemic control groups. After multivariate adjustment, a higher A1C was associated with an increased incidence of DSWI (hazard ratio = 1.38, P = 0.009).Conclusion In cardiac surgery patients with DM, despite standardized control of immediate postoperative hyperglycemia, a high preoperative A1C was associated with an increased incidence of DSWI. Cardiovasc Endocrinol 2:15-22 c 2013 Wolters Kluwer Health | Lippincott Williams & Wilkins. Cardiovascular Endocrinology 2013, 2:15-22
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