Preoperative Diabetes Optimization Program

A Systematic Guideline by the ASPN Workgroup on the Evidence, Education, and Treatment Algorithm for Painful Diabetic Neuropathy: SWEET

Introduction

Painful diabetic neuropathy (PDN) is a leading cause of pain and disability globally with a lack of consensus on the appropriate treatment of those suffering from this condition. Recent advancements in both pharmacotherapy and interventional approaches have broadened the treatment options for PDN. There exists a need for a comprehensive guideline for the safe and effective treatment of patients suffering from PDN.

Objective

The SWEET Guideline was developed to provide clinicians with the most comprehensive guideline for the safe and appropriate treatment of patients suffering from PDN.

Methods

The American Society of Pain and Neuroscience (ASPN) identified an educational need for a comprehensive clinical guideline to provide evidence-based recommendations for PDN. A multidisciplinary group of international experts developed the SWEET guideline. The world literature in English was searched using Medline, EMBASE, Cochrane CENTRAL, BioMed Central, Web of Science, Google Scholar, PubMed, Current Contents Connect, Meeting Abstracts, and Scopus to identify and compile the evidence for diabetic neuropathy pain treatments (per section as listed in the manuscript) for the treatment of pain. Manuscripts from 2000-present were included in the search process.

Results

After a comprehensive review and analysis of the available evidence, the ASPN SWEET guideline was able to rate the literature and provide therapy grades for most available treatments for PDN utilizing the United States Preventive Services Task Force criteria.

Conclusion

The ASPN SWEET Guideline represents the most comprehensive review of the available treatments for PDN and their appropriate and safe utilization.

Preoperative Optimization for Orthopaedic Surgery: Steps to Reduce Complications

As the population ages and patients maintain higher levels of activity, the incidence of major and minor orthopaedic procedures continues to rise. At the same time, health policies are incentivizing efforts to improve the quality and value of musculoskeletal health services. As such, orthopaedic surgeons play a key role in directing the optimization of patients before surgery by assessing patient risk factors to inform risk/benefit discussions during shared decision-making and designing optimization programs to address modifiable risks. These efforts can lead to improved health outcomes, reduced costs, and preference-congruent treatment decisions. In this review, we (1) summarize the evidence on factors known to affect outcomes after common orthopaedic procedures, (2) identify which factors are considered modifiable and amenable to preoperative intervention, and (3) provide guidance for preoperative optimization.

Practical Considerations of Perioperative Assessment and Optimization in Major Colorectal Surgery

Successful outcomes after colorectal surgery result not only from technique in the operating room, but also from optimization of the patient prior to surgery. This article will discuss the role of preoperative assessment and optimization in the colorectal surgery patient. Through discussion of the various clinical models, readers will understand the range of options available for optimization. This study will also present information on how to design a preoperative clinic and the barriers to success.

Managing Patients Undergoing Orthopedic Surgery to Improve Glycemic Outcomes

PURPOSE OF REVIEW

Persons with diabetes are more likely to require orthopedic surgery and are at an increased risk of developing postoperative complications. Recognizing the impact of diabetes on musculoskeletal health provides an opportunity to educate healthcare professionals in standardizing the perioperative approach of persons with diabetes.

RECENT FINDINGS

Elevated hemoglobin A1C, fructosamine, and blood glucose levels have been associated with increased risk for complications in the orthopedic population. These risks can be mitigated by the early identification and optimization of these patients in the perioperative period. Intraoperative and postoperative glycemic management should support efforts to maintain glucose at safe levels while avoiding hyperglycemia and hypoglycemia. This paper considers factors surrounding diabetes care in the orthopedic surgical patient. Perioperative care discussed includes optimization, hospitalization to discharge, and special considerations such as steroids and diabetes wearable technology. Hospitals should consider these strategies towards enhancing the care of persons with diabetes requiring musculoskeletal care.

Developing an intervention to optimise the outcome of cardiac surgery in people with diabetes: the OCTOPuS pilot study

BACKGROUND

Cardiothoracic surgical outcomes are poorer in people with diabetes compared with those without diabetes. There are two important uncertainties in the management of people with diabetes undergoing major surgery: (1) how to improve diabetes management in the weeks leading up to an elective procedure and (2) whether that improved management leads to improved postoperative outcomes. The aim of this study was to develop and pilot a specialist diabetes team-led intervention to improve surgical outcomes in people with diabetes.

DESIGN

Open pilot feasibility study SETTING: Diabetes and cardiothoracic surgery departments, University Hospital Southampton NHS Foundation Trust PARTICIPANTS: Seventeen people with diabetes undergoing cardiothoracic surgery INTERVENTION: Following two rapid literature reviews, a prototype intervention was developed based on a previously used nurse-led outpatient intervention and tested.

PRIMARY OUTCOME

Feasibility and acceptability of delivering the intervention SECONDARY OUTCOMES: Biomedical data were collected at baseline and prior to surgery. We assessed how the intervention was used. In depth qualitative interviews with participants and healthcare professionals were used to explore perceptions and experiences of the intervention and how it might be improved.

RESULTS

Thirteen of the 17 people recruited completed the study and underwent cardiothoracic surgery. All components of the OCTOPuS intervention were used, but not all parts were used for all participants. Minor changes were made to the intervention as a result of feedback from the participants and healthcare professionals. Median (IQR) HbA1c was 10 mmol/mol (3, 13) lower prior to surgery than at baseline.

CONCLUSION

This study has shown that it is possible to develop a clinical pathway to improve diabetes management prior to admission. The clinical and cost-effectiveness of this intervention will now be tested in a multicentre randomised controlled trial in cardiothoracic centres across the UK.

TRIAL REGISTRATION

ISRCTN; ISRCTN10170306 . Registered 10 May 2018.

On All Accounts: Cost-Effectiveness Analysis of Limited Preoperative Optimization Efforts Before Colon Cancer Surgery

BACKGROUND

Reports suggest that preoperative optimization of a patient's serious comorbidities is associated with a reduction in postoperative complications.

OBJECTIVE

The purpose of this study was to assess the cost and benefits of preoperative optimization, accounting for total costs associated with postoperative morbidity.

DESIGN

This study is a decision tree cost-effectiveness analysis with probabilistic sensitivity analysis (10,000 iterations).

SETTING

This is a hypothetical scenario of stage II colon cancer surgery.

PATIENT

The simulated 65-year-old patient has left-sided, stage II colon cancer.

INTERVENTION

Focused preoperative optimization targets high-risk comorbidities.

OUTCOMES

Total discounted (3%) economic costs (US $2018), effectiveness (quality-adjusted life-years), incremental cost-effectiveness ratio (incremental cost-effectiveness ratio, cost/quality-adjusted life-years gained), and net monetary benefit.

RESULTS

We calculated the per individual expected health care sector total cost of preoperative optimization and sequelae to be $12,395 versus $15,638 in those not optimized (net monetary benefit: $1.04 million versus $1.05 million). A nonoptimized patient attained an average 0.02 quality-adjusted life-years less than one optimized. Thus, preoperative optimization was the dominant strategy (lower total costs; higher quality-adjusted life-years). Probabilistic sensitivity analysis demonstrated 100% of simulations favoring preoperative optimization. The breakeven cost of optimization to remain cost-effective was $6421 per patient.

LIMITATIONS

Generalizability must account for the lack of standardization among existing preoperative optimization efforts, and decision analysis methodology provides guidance for the average patient or general population, and is not patient-specific.

CONCLUSIONS

Although currently not comprehensively reimbursed, focused preoperative optimization may reduce total costs of care while also reducing complications from colon cancer surgery. See Video Abstract at http://links.lww.com/DCR/B494.

EN TODO CASO ANLISIS DE RENTABILIDAD DE LOS ESFUERZOS LIMITADOS DE OPTIMIZACIN PREOPERATORIA ANTES DE LA CIRUGA DE CNCER DE COLON

ANTECEDENTES:Los informes sugieren que la optimización preoperatoria de las comorbilidades graves de un paciente se asocia con una reducción de las complicaciones postoperatorias.OBJETIVO:El propósito de este estudio fue evaluar el costo y los beneficios de la optimización preoperatoria, teniendo en cuenta los costos totales asociados con la morbilidad postoperatoria.DISEÑO:Análisis de costo-efectividad de árbol de decisión con análisis de sensibilidad probabilístico (10,000 iteraciones).AJUSTE ENTORNO CLINICO:Escenario hipotético Cirugía de cáncer de colon en estadio II.PACIENTE:Paciente simulado de 65 años con cáncer de colon en estadio II del lado izquierdo.INTERVENCIÓN:Optimización preoperatoria enfocada dirigida a comorbilidades de alto riesgo.RESULTADOS:Costos económicos totales descontados (3%) (US $ 2018), efectividad (años de vida ajustados por calidad [AVAC]), relación costo-efectividad incremental (ICER, costo / AVAC ganado) y beneficio monetario neto (NMB).RESULTADOS:Calculamos que el costo total esperado por sector de atención médica individual de la optimización preoperatoria y las secuelas es de $ 12,395 versus $ 15,638 en aquellos no optimizados (NMB: $ 1.04 millones versus $ 1.05 millones, respectivamente). Un paciente no optimizado alcanzó un promedio de 0.02 AVAC menos que uno optimizado. Por lo tanto, la optimización preoperatoria fue la estrategia dominante (menores costos totales; mayores AVAC). El análisis de sensibilidad probabilístico demostró que el 100% de las simulaciones favorecían la optimización preoperatoria. El costo de equilibrio de la optimización para seguir siendo rentable fue de $ 6,421 por paciente.LIMITACIONES:La generalización debe tener en cuenta la falta de estandarización entre los esfuerzos de optimización preoperatorios existentes y esa metodología de análisis de decisiones proporciona una guía para el paciente promedio o la población general, no específica del paciente.CONCLUSIONES:Si bien actualmente no se reembolsa de manera integral, la optimización preoperatoria enfocada puede reducir los costos totales de la atención y al mismo tiempo reducir las complicaciones de la cirugía de cáncer de colon. Consulte Video Resumen en http://links.lww.com/DCR/B494.

Web-Based Calculator Predicts Surgical-Site Infection After Thoracolumbar Spine Surgery

BACKGROUND

Surgical-site infection (SSI) after spine surgery leads to increased length of stay, reoperation, and worse patient quality of life. We sought to develop a web-based calculator that computes an individual's risk of a wound infection following thoracolumbar spine surgery.

METHODS

We performed a retrospective review of consecutive patients undergoing elective degenerative thoracolumbar spine surgery at a tertiary-care institution between January 2016 and December 2018. Patients who developed SSI requiring reoperation were identified. Regression analysis was performed and model performance was assessed using receiver operating curve analysis to derive an area under the curve. Bootstrapping was performed to check for overfitting, and a Hosmer-Lemeshow test was employed to evaluate goodness-of-fit and model calibration.

RESULTS

In total, 1259 patients were identified; 73% were index operations. The overall infection rate was 2.7%, and significant predictors of SSI included female sex (odds ratio [OR] 3.0), greater body mass index (OR 1.1), active smoking (OR 2.8), worse American Society of Anesthesiologists physical status (OR 2.1), and greater surgical invasiveness (OR 1.1). The prediction model had an optimism-corrected area under the curve of 0.81. A web-based calculator was created: https://jhuspine2.shinyapps.io/Wound_Infection_Calculator/.

CONCLUSIONS

In this pilot study, we developed a model and simple web-based calculator to predict a patient's individualized risk of SSI after thoracolumbar spine surgery. This tool has a predictive accuracy of 83%. Through further multi-institutional validation studies, this tool has the potential to alert both patients and providers of an individual's SSI risk to improve informed consent, mitigate risk factors, and ultimately drive down rates of SSIs.

Duke University Medical Center Perioperative Diabetes Management Program

Quality Improvement Success Stories are published by the American Diabetes Association in collaboration with the American College of Physicians and the National Diabetes Education Program. This series is intended to highlight best practices and strategies from programs and clinics that have successfully improved the quality of care for people with diabetes or related conditions. Each article in the series is reviewed and follows a standard format developed by the editors of Clinical Diabetes. The following article describes a project at an academic tertiary-care medical center aimed at identifying surgical patients with uncontrolled diabetes early in the preoperative process to improve their perioperative glycemic control and surgical outcomes.

A prospective observational prevalence study of elevated HbA1c among elective surgical patients

Type 2 Diabetes Mellitus (DM) is a chronic disease with high prevalence worldwide. Using glycated haemoglobin (HbA1c) as a surrogate for potential pre-DM and DM conditions, our primary objective was to determine the HbA1c epidemiology in non-cardiac elective surgical patients in Singapore. Our secondary aim was to identify risk factors associated with elevated HbA1c. We conducted a prospective, observational single-centre study in adult patients. HbA1c screening was performed. Patient demographics and comorbidities were recorded. Patients were divided into those with HbA1C ≤ 6.0% and HbA1C ≥ 6.1%. Regression analyses were performed to identify associated factors. Subgroup analysis was performed comparing patients with HbA1C ≥ 6.1% and HbA1C ≥ 8.0%. Of the 875 patients recruited, 182 (20.8%) had HbA1c ≥ 6.1%, of which 32 (3.7%) had HbA1c ≥ 8%. HbA1C ≥ 6.1% was associated with Indian ethnicity [1.07 (1.01-1.13), p = 0.023], BMI > 27.5 [1.07 (1.02-1.11), p = 0.002], higher preoperative random serum glucose [1.03 (1.02-1.04), p < 0.001], pre-existing diagnosis of DM [1.85 (1.75-1.96), p < 0.001] and prediabetes [1.44 (1.24-1.67), p < 0.001], and peripheral vascular disease [1.30 (1.10-1.54), p = 0.002]. HbA1c ≥ 8% had an additional association with age > 60 years [0.96 (0.93-0.99), p = 0.017]. The prevalence of elevated HbA1c is high among the surgical population. Targeted preoperative HbA1c screening for at-risk elective surgical patients reduces cost, allowing focused use of healthcare resources.

Hospital Diabetes Meeting 2020

Patients with diabetes may experience adverse outcomes related to their glycemic control when hospitalized. Continuous glucose monitoring systems, insulin-dosing software, enhancements to the electronic health record, and other medical technologies are now available to improve hospital care. Because of these developments, new approaches are needed to incorporate evolving treatments into routine care. With the goal of educating healthcare professionals on the most recent practices and research for managing diabetes in the hospital, Diabetes Technology Society hosted the Virtual Hospital Diabetes Meeting on April 24-25, 2020. Because of the coronavirus disease 2019 (COVID-19) pandemic, the meeting was restructured to be held virtually during the national lockdown to ensure the safety of the participants and allow them to remain at their posts treating COVID-19 patients. The meeting focused on (1) inpatient management and perioperative care, (2) diabetic ketoacidosis and hyperglycemic hyperosmolar state, (3) computer-guided insulin dosing, (4) Coronavirus Disease 2019 and diabetes, (5) technology, (6) hypoglycemia, (7) data and cybersecurity, (8) special situations, (9) glucometrics and insulinometrics, and (10) quality and safety. This meeting report contains summaries of each of the ten sessions. A virtual poster session will be presented within two months of the meeting.

The neglected perioperative population of undiagnosed diabetics - a retrospective cohort study

BACKGROUND

Diabetes is known to increase morbidity and 30-day mortality in adults undergoing non-cardiac surgery, but longer term outcomes are less studied. This study was done to explore how undiagnosed and known diabetes affect 30-day and one-year morbidity and mortality outcomes. The secondary aim was to study the prevalence of undiagnosed diabetics in our perioperative Asian surgical population.

METHODS

A retrospective cohort study of 2106 patients aged > 45 years undergoing non-cardiac surgery in a single tertiary hospital was performed. Undiagnosed diabetics were identified (HbA1c ≥6.5% or fasting blood glucose ≥126 mg/dL) and relevant demographic, clinical and surgical data were analyzed to elicit the relationship to adverse outcomes. Univariate analysis was first performed to identify significant variables with p-values ≤0.1, which were then analyzed using multiple logistic regression to calculate the adjusted odds ratio.

RESULTS

The prevalence of undiagnosed diabetes was 7.4%. The mean and median HbA1c of known diabetics were 7.9 and 7.5%, while the mean and median HbA1c for undiagnosed diabetics were 7.2 and 6.8% respectively. 36.4% of known diabetics and 20.5% of undiagnosed diabetics respectively had a random blood glucose > 200 mg/dL. Undiagnosed diabetics had a three-fold increase in 1-year mortality compared to non-diabetics (adjusted OR 3.46(1.80-6.49) p < 0.001) but this relationship was not significant between known and non-diabetics. Compared to non-diabetics, known diabetics were at increased risks of new-onset atrial fibrillation (aOR 2.48(1.01-6.25) p = 0.047), infection (aOR 1.49(1.07-2.07) p = 0.017), 30-day readmission (aOR 1.62(1.17-2.25) p = 0.004) and 30-day mortality (aOR 3.11(1.16-8.56) p = 0.025).

CONCLUSIONS

Although undiagnosed diabetics have biochemically less severe disease compared to known diabetics at the point of testing, they are at a one-year mortality disadvantage which is not seen among known diabetics. This worrying trend highlights the importance of identifying and treating diabetes. Congruent to previous studies, known diabetics have higher morbidity and 30-day mortality compared to non-diabetics.