Narcotics reduction, quality and safety in gynecologic oncology surgery in the first year of enhanced recovery after surgery protocol implementation.

OBJECTIVES: Enhanced Recovery After Surgery (ERAS) programs are mechanisms for achieving value-based improvements in surgery. This report provides a detailed analysis of the impact of an ERAS program on patient outcomes as well as quality and safety measures during implementation on a gynecologic oncology service at a major academic medical center. METHODS: A retrospective review of gynecologic oncology patients undergoing elective laparotomy during the implementation phase of an ERAS program (January 2016 through December 2016) was performed. Patient demographics, surgical variables, postoperative outcomes, and adherence to core safety measures, including antimicrobial and venous thromboembolism (VTE) prophylaxis, were compared to a historical patient cohort (January 2015 through December 2015). Statistical analyses were performed using t-tests, Wilcoxon rank sum tests, and Chi squared tests. RESULTS: The inaugural 109 ERAS program participants were compared to a historical patient cohort (n=158). There was no difference in BMI, race, malignancy, or complexity of procedure between cohorts. ERAS patients required less narcotics (70.7 vs 127.4, p=0.007, oral morphine equivalents) and PCA use (32.1% vs. 50.6%, p=0.002). Despite this substantial reduction in narcotics, ERAS patients did not report more pain and in fact reported significantly less pain by postoperative day 3. There were no differences in length of stay (5days), complication rates (13.8% vs. 20.3%, p=0.17) or 30-day readmission rates (9.5 vs 11.9%, p=0.54) between ERAS and historical patients, respectively. Compliance with antimicrobial prophylaxis was 97.2%. However, 33.9% of ERAS patients received substandard preoperative VTE prophylaxis. CONCLUSIONS: ERAS program implementation resulted in reductions in narcotic requirements and PCA use without changes in length of stay or readmission rates. Compliance should be diligently audited during the implementation phase of ERAS programs, with special attention to adherence to pre-existing core safety measures.

Development and Validation of a Machine Learning Model to Predict Near-Term Risk of Iatrogenic Hypoglycemia in Hospitalized Patients.

Importance: Accurate clinical decision support tools are needed to identify patients at risk for iatrogenic hypoglycemia, a potentially serious adverse event, throughout hospitalization. Objective: To predict the risk of iatrogenic hypoglycemia within 24 hours after each blood glucose (BG) measurement during hospitalization using a machine learning model. Design, Setting, and Participants: This retrospective cohort study, conducted at 5 hospitals within the Johns Hopkins Health System, included 54 978 admissions of 35 147 inpatients who had at least 4 BG measurements and received at least 1 U of insulin during hospitalization between December 1, 2014, and July 31, 2018. Data from the largest hospital were split into a 70% training set and 30% test set. A stochastic gradient boosting machine learning model was developed using the training set and validated on internal and external validation. Exposures: A total of 43 clinical predictors of iatrogenic hypoglycemia were extracted from the electronic medical record, including demographic characteristics, diagnoses, procedures, laboratory data, medications, orders, anthropomorphometric data, and vital signs. Main Outcomes and Measures: Iatrogenic hypoglycemia was defined as a BG measurement less than or equal to 70 mg/dL occurring within the pharmacologic duration of action of administered insulin, sulfonylurea, or meglitinide. Results: This cohort study included 54 978 admissions (35 147 inpatients; median [interquartile range] age, 66.0 [56.0-75.0] years; 27 781 [50.5%] male; 30 429 [55.3%] White) from 5 hospitals. Of 1 612 425 index BG measurements, 50 354 (3.1%) were followed by iatrogenic hypoglycemia in the subsequent 24 hours. On internal validation, the model achieved a C statistic of 0.90 (95% CI, 0.89-0.90), a positive predictive value of 0.09 (95% CI, 0.08-0.09), a positive likelihood ratio of 4.67 (95% CI, 4.59-4.74), a negative predictive value of 1.00 (95% CI, 1.00-1.00), and a negative likelihood ratio of 0.22 (95% CI, 0.21-0.23). On external validation, the model achieved C statistics ranging from 0.86 to 0.88, positive predictive values ranging from 0.12 to 0.13, negative predictive values of 0.99, positive likelihood ratios ranging from 3.09 to 3.89, and negative likelihood ratios ranging from 0.23 to 0.25. Basal insulin dose, coefficient of variation of BG, and previous hypoglycemic episodes were the strongest predictors. Conclusions and Relevance: These findings suggest that iatrogenic hypoglycemia can be predicted in a short-term prediction horizon after each BG measurement during hospitalization. Further studies are needed to translate this model into a real-time informatics alert and evaluate its effectiveness in reducing the incidence of inpatient iatrogenic hypoglycemia.

Effects of a Dedicated Inpatient Diabetes Management Service on Glycemic Control in a Community Hospital Setting.

BACKGROUND: Community hospitals account for over 84% of all hospitals and over 94% of hospital admissions in the United States. In academic settings, implementation of an Inpatient Diabetes Management Service (IDMS) model of care has been shown to reduce rates of hyper- and hypoglycemia, hospital length of stay (LOS), and associated hospital costs. However, few studies to date have evaluated the implementation of a dedicated IDMS in a community hospital setting. METHODS: This retrospective study examined the effects of changing the model of inpatient diabetes consultations from a local, private endocrine practice to a full-time endocrine hospitalist on glycemic control, LOS, and 30-day readmission rates in a 267-bed community hospital. RESULTS: Overall diabetes patient days for the hospital were similar pre- and post-intervention (20,191 vs 20,262); however, the volume of patients seen by IDMS increased significantly after changing models. Rates of hyperglycemia decreased both among patients seen by IDMS (53.8% to 42.5%, P < .0001) and those not consulted on by IDMS (33.2% to 29.9%; P < .0001). When examined over time, rates of hypoglycemia steadily decreased in the 24 months after dedicated IDMS initiation (P = .02); no such time effect was seen prior to IDMS (P = .34). LOS and 30DRR were not significantly different between IDMS models. CONCLUSIONS: Implementation of an endocrine hospitalist-based IDMS at a community hospital was associated with significantly decreased hyperglycemia, while avoiding concurrent increases in hypoglycemia. Further studies are needed to investigate whether these effects are associated with improvements in clinical outcomes, patient or staff satisfaction scores, or total cost of care.

Retrospective Quality Improvement Study of Insulin-Induced Hypoglycemia and Implementation of Hospital-Wide Initiatives.

BACKGROUND: Hospitalized patients who are receiving antihyperglycemic agents are at increased risk for hypoglycemia. Inpatient hypoglycemia may lead to increased risk for morbidity, mortality, prolonged hospitalization, and readmission within 30 days of discharge, which in turn may lead to increased costs. Hospital-wide initiatives targeting hypoglycemia are known to be beneficial; however, their impact on patient care and economic measures in community nonteaching hospitals are unknown. METHODS: This retrospective quality improvement study examined the effects of hospital-wide hypoglycemia initiatives on the rates of insulin-induced hypoglycemia in a community hospital setting from January 1, 2016, until September 30, 2019. The potential cost of care savings has been calculated. RESULTS: Among 49 315 total patient days, 2682 days had an instance of hypoglycemia (5.4%). Mean ± SD hypoglycemic patient days/month was 59.6 ± 16.0. The frequency of hypoglycemia significantly decreased from 7.5% in January 2016 to 3.9% in September 2019 (P = .001). Patients with type 2 diabetes demonstrated a significant decrease in the frequency of hypoglycemia (7.4%-3.8%; P < .0001), while among patients with type 1 diabetes the frequency trended downwards but did not reach statistical significance (18.5%-18.0%; P = 0.08). Based on the reduction of hypoglycemia rates, the hospital had an estimated cost of care savings of $98 635 during the study period. CONCLUSIONS: In a community hospital setting, implementation of hospital-wide initiatives targeting hypoglycemia resulted in a significant and sustainable decrease in the rate of insulin-induced hypoglycemia. These high-leverage risk reduction strategies may be translated into considerable cost savings and could be implemented at other community hospitals.

Development, Implementation, and Evaluation of a Physician-Targeted Inpatient Glycemic Management Curriculum.

OBJECTIVE: Diabetes is prevalent among hospitalized patients and there are multiple challenges to attaining glycemic control in the hospital setting. We sought to develop an inpatient glycemic management curriculum with stakeholder input and to evaluate the effectiveness of this educational program on glycemic control in hospitalized patients. METHODS: Using the Six-Step Approach of Kern to Curriculum Development for Medical Education, we developed and implemented an educational curriculum for inpatient glycemic management targeted to internal medicine residents and hospitalists. We surveyed physicians (n = 73) and conducted focus group sessions (n = 18 physicians) to solicit input regarding educational deficits and desired format of the educational intervention. Based on feedback from the surveys and focus groups, we developed educational goals and objectives and a case-based curriculum, which was delivered over a 1-year period via in-person teaching sessions by 2 experienced diabetes physicians at 3 hospitals. Rates of hypoglycemia and hyperglycemia were evaluated among at-risk patient days using an interrupted time-series design. RESULTS: We developed a mnemonic-based (SIGNAL) curriculum consisting of 10 modules, which covers key concepts of inpatient glycemic management and provides an approach to daily glycemic management: S = steroids, I = insulin, G = glucose, N = nutritional status, A = added dextrose, and L = labs. Following implementation of the curriculum, there was no difference in the rates of hyperglycemia in insulin-treated patients following the intervention; however, there was an increase in the rates of hypoglycemia defined as blood glucose (BG) ⩽ 70 mg/dL (5.6% vs 3.0%, P < .001) and clinically significant hypoglycemia defined as BG < 54 mg/dL (1.9% vs 0.8%, P = .01). There was poor penetration of the curriculum, with 60%, 20%, and 90% of the learning modules being delivered at the three participating hospitals, respectively. CONCLUSIONS: In this pilot study, a physician-targeted educational curriculum was not associated with improved glycemic control. Adapting the intervention to increase penetration and integrating the curriculum into existing clinical decision support tools may improve the effectiveness of the educational program on glycemic outcomes.