Predicting and Preventing Acute Care Re-Utilization by Patients with Diabetes

Determinants and predictors of early re-admission of patients with hyperglycemic crises: a machine learning-based analysis

Objectives

The predictors of early re-admission of patients with diabetes mellitus (DM) have been studied with classical statistical techniques. Considering the increasing application of artificial intelligence to drive advances in medicine, this study aimed to leverage machine learning techniques to identify patients at risk of early re-admission after being admitted for hyperglycemic crises.

Methods

We extracted relevant data from a publicly available dataset of patients with DM who were admitted in U.S. hospitals from 1999 to 2008. The target variable was re-admission within 30 days. Point-biserial and chi-square tests were used to assess correlations between the input and target variables. Three machine learning models were initially deployed; the model with the best recall for the positive class was selected.

Results

The prevalence of early re-admission among the patients was 13.32%. Statistical tests revealed weak correlations between early re-admission and race, sex, age, use of antidiabetic medication, and numbers of non-laboratory procedures, medications, diagnoses, and visits to the emergency and inpatient departments in the previous year (all p < 0.05). Extreme gradient boosting classifier predicted early-re-admission with 79% recall for the positive class. The area under the receiver-operating characteristic curve was 0.78. Age and numbers of medications, emergency and inpatient visits in the previous year, and non-laboratory procedures, were the most important features for the model's prediction.

Conclusions

Our findings highlight the usefulness of machine learning in making clinical decisions in the management of patients with diabetes, especially when classical statistical methods do not yield much significant information.

Nurse-Led Care Coordination in a Transitional Clinic for Uninsured Patients With Diabetes

PURPOSE/OBJECTIVES

The purpose of this article is to inform the reader of the practice of the registered nurse care coordinator (RNCC) within an interprofessional, nurse-led clinic serving uninsured diabetic patients in a large urban city. This clinic serves as a transitional care clinic, providing integrated diabetes management and assisting patients to establish with other primary care doctors in the community once appropriate. The clinic uses an interprofessional collaborative practice (IPCP) model with the RNCC at the center of patient onboarding, integrated responsive care, and clinic transitioning.

PRIMARY PRACTICE SETTING

Interprofessional, nurse-led clinic for uninsured patients with diabetes.

FINDINGS/CONCLUSIONS

Interprofessional models of care are strengthened using a specialized care coordinator.

IMPLICATIONS FOR CASE MANAGEMENT PRACTICE

Care coordination is a key component in case management of a population with chronic disease. The RNCC, having specialized clinical expertise, is an essential member of the interdisciplinary team, contributing a wide range of resources to assist patients in achieving successful outcomes managing diabetes. Transitional care coordination, moving from unmanaged to managed diabetes care, is part of a bundled health care process fundamental to this clinic's IPCP model. In a transitional clinic setting, frequent interaction with patients through onboarding, routine check-ins, and warm handoff helps support and empower the patient to be engaged in their personal health care journey.

SMART DIABETES HOSPITAL: CLINICAL IMPACT IN COMPLEX SURGICAL UNITS OF A TERTIARY HOSPITAL

AIM

To evaluate the impact of a proactive action of a specialized diabetes team (SDT) on different health outcomes in patients hospitalized in high complexity surgery units, including solid organ transplant surgical units, of a tertiary hospital.

METHODS

Nested case control study matched (1:1) by age and gender. The control group consisted of patients (n = 120) who were under the standard of care diabetes management admitted three months' prior the cases. The cases were admitted in the same surgical units (n = 120) and were treated in the setting of the so called "Smart Diabetes Hospital" (SDH) consisting in a SDT that prioritized their actions through a digital map showing blood glucose levels obtained during the previous 24 h.

RESULTS

SDH implementation resulted in a significant reduction in both blood glucose levels (mean 162.1 ± SD 44.4 vs. mean 145.5 ± SD 48.0; p = 0.008) and hypoglycaemic episodes (19.7% vs. 8.4%: p = 0.002). Furthermore, a reduction of 3 days in the length of stay (LOS) was observed (15.6 ± 10.3 vs. 12.4 ± 6.0), which represents a significant cost-saving. Moreover, more new cases of diabetes were detected during the SDT period (2.5% vs. 6.7%, p = 0.04).

CONCLUSION

SDH is effective in diabetes management and reduce LOS in complex surgical units.

Nomogram Predicting 90-Day Readmission in Patients with Diabetes: A Prospective Study

Purpose

Readmission within a period time of discharge is common and costly. Diabetic patients are at risk of readmission because of comorbidities and complications. It is crucial to monitor patients with diabetes with risk factors for readmission and provide them with target suggestions. We aim to develop a nomogram to predict the risk of readmission within 90 days of discharge in diabetic patients.

Patients and Methods

This is a prospective observational survey. A total of 784 adult patients with diabetes recruited in two tertiary hospitals in central China were randomly assigned to a training set or a validation set at a ratio of 7:3. Depression, anxiety, self-care, physical activity, and sedentary behavior were assessed during hospitalization. A 90-day follow-up was conducted after discharge. Multivariate logistic regression was employed to develop a nomogram, which was validated with the use of a validation set. The AUC, calibration plot, and clinical decision curve were used to assess the discrimination, calibration, and clinical usefulness of the nomogram, respectively.

Results

In this study, the 90-day readmission rate in our study population was 18.6%. Predictors in the final nomogram were previous admissions within 1 year of the index admission, self-care scores, anxiety scores, physical activity, and complicating with lower extremity vasculopathy. The AUC values of the predictive model and the validation set were 0.905 (95% CI=0.874-0.936) and 0.882 (95% CI=0.816-0.947). Hosmer-Lemeshow test values were p = 0.604 and p = 0.308 (both > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. Decision curve analysis indicated that the nomogram improved the clinical net benefit within a probability threshold of 0.02-0.96.

Conclusion

The nomogram constructed in this study was a convenient tool to evaluate the risk of 90-day readmission in patients with diabetes and contributed to clinicians screening the high-risk populations.

American Diabetes Association Professional Practice Committee, ElSayed NA, McCoy RG, Aleppo G, Balapattabi K, Beverly EA, Briggs Early K, Bruemmer D, Echouffo-Tcheugui JB, Ekhlaspour L, Galindo RJ, Garg R, Khunti K, Lal R, Lingvay I, Matfin G, Pandya N, Pekas EJ, Pilla SJ, Polsky S, Segal AR, Seley JJ, Stanton RC, Bannuru RR. 16. Diabetes Care in the Hospital: Standards of Care in Diabetes-2025

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Domain generalization for enhanced predictions of hospital readmission on unseen domains among patients with diabetes

A prediction model to assess the risk of hospital readmission can be valuable to identify patients who may benefit from extra care. Developing hospital-specific readmission risk prediction models using local data is not feasible for many institutions. Models developed on data from one hospital may not generalize well to another hospital. There is a lack of an end-to-end adaptable readmission model that can generalize to unseen test domains. We propose an early readmission risk domain generalization network, ERR-DGN, for cross-domain knowledge transfer. ERR-DGN internalizes the shared patterns and characteristics that are consistent across source domains, enabling it to adapt to a new domain. It transforms source datasets to a common embedding space while capturing relevant temporal long-term dependencies of sequential data. Domain generalization is then applied on domain-specific fully connected linear layers. The model is optimized by a loss function that integrates distribution discrepancy loss to match the mean embeddings of multiple source distributions with the task-specific loss. A model was developed using electronic health record (EHR) data of 201,688 patients with diabetes across urban, suburban, rural, and mixed hospital systems to enhance 30-day readmission predictions among patients with diabetes on 67,066 unseen patients at a rural hospital. We also explored how model performance varied by the number of sites and over time. The proposed method outperformed the baseline models, yielding a 6 % increase in F1-score (0.79 ± 0.006 vs. 0.73 ± 0.007). Model performance peaked with the inclusion of three sites. Performance of the model was relatively stable for 3 years then declined at 4 years. ERR-DGN may be a proficient tool for learning data from multiple sites and subsequently applying a hospitalization readmission prediction model to a new site. Including a relatively small number of varied sites may be sufficient to achieve peak performance. Periodic retraining at least every 3 years may mitigate model degradation over time.

The Impact of Health Education on Hemoglobin A1C in Diabetic Patients at the Family Medicine Department of King Fahad Armed Forces Hospital in Jeddah

BACKGROUND

Diabetes mellitus (DM) is a long-term condition associated with severe complications. Individuals with diabetes must make numerous self-management decisions and participate in diverse care activities. Diabetes self-management education and support assist patients in making these decisions and performing these activities, enhancing their health outcomes. The study aims to assess the effects of health education on median hemoglobin A1c (HbA1C) levels, the development of diabetic complications, and the number of hospital admissions in patients with uncontrolled type 2 DM.

METHODS

This prospective quasi-experimental study, conducted at King Fahad Armed Forces Hospital in Jeddah, Saudi Arabia, from September 2020 to September 2022, assessed the impact of a structured diabetic education program on uncontrolled type 2 DM patients. The study involved 100 patients with HbA1c >8%: 50 in the intervention group who received the program and 50 in the control group who did not. HbA1c levels were measured before and after the intervention. Data was collected securely, and an experienced biostatistician performed statistical analysis.

FINDINGS

The two groups found no significant differences in age, disease duration, HbA1c, medication type, insulin type, education level, employment, and clinical visits. However, the control group had significantly more females (40 (81.6%) vs. 32 (64%), p=0.049), and the intervention group had larger family sizes (43 (86%) with >4 members vs. 21 (42.9%), p<0.0001). The intervention group showed a significant decrease in HbA1c from baseline across all measurements post-educational program (p<0.0001), whereas the control group did not show significant changes. Economic status also differed significantly (p=0.024). No significant differences were found between groups in follow-up HbA1c measurements.

CONCLUSION

The study demonstrates that the diabetic education program at King Fahad Armed Forces Hospital effectively lowered HbA1c levels in type 2 DM patients, confirming the program's role in enhancing glycemic control through structured self-management education and support.

Outcomes of the Dulce Digital-COVID Aware (DD-CA) discharge texting platform for US/Mexico border Hispanic individuals with diabetes

BACKGROUND

Hispanic individuals have higher type 2 diabetes (T2D) prevalence, poorer outcomes, and are disproportionately affected by COVID-19. Culturally-tailored, diabetes educational text messaging has previously improved HbA1c in this population.

METHODS

During the pandemic, hospitalized Hispanic adults with T2D (N = 172) were randomized to receive Dulce Digital-COVID Aware ("DD-CA") texting platform upon discharge plus diabetes transition service (DTS) or DTS alone. DD-CA includes diabetes educational messaging with additional COVID-safe messaging (e.g., promoting masking; social distancing; vaccination).

FINDINGS

Among adults with poorly-controlled diabetes (Mean HbA1c = 9.6 ± 2.2 %), DD-CA did not reduce 30- or 90-day readmissions compared to standard care (28 % vs 15 %, p = .06; 37 % vs 35 %, p = .9, respectively). However, the improvement in HbA1c was larger among those in the DD-CA compared to DTS at 3 months (n = 56; -2.69 % vs. -1.45 %, p = .0496) with reduced effect at 6 months (n = 64; -2.03 % vs -0.91 %, p = .07). Low follow-up completion rates and the addition of covariates (to control for baseline group differences that existed despite randomization) impacted statistical power.

INTERPRETATION

During the pandemic, DD-CA offered an alternative digital approach to diabetes and COVID education and support for a high-risk Hispanic population and achieved trends toward improvement in glycemic control despite relatively low engagement and not reducing hospital readmissions.

Hospitalisation for herpes zoster in people with and without diabetes: A 10-year-observational study

AIMS

This study explores the association between Herpes Zoster (HZ) hospitalizations and diabetes in Piedmont, Italy from 2010 to 2019. Focusing on the burden of HZ hospitalizations in diabetic and non-diabetic groups, it aims to identify risk factors in diabetics to enhance prevention strategies.

METHODS

In a two-phase study, we first compared age-standardized HZ hospitalization rates between diabetic and non-diabetic individuals from 2010 to 2019. We then examined hospitalization risk factors for HZ within a diabetic patient cohort managed by regional diabetes clinics.

RESULTS

Of 3,423 HZ hospitalizations in 2010-2019, 17.9 % (613 cases) were diabetic patients, who exhibited higher hospitalization rates (15.9 to 6.0 per 100,000) compared to non-diabetese individuals. Among diabetics subjects risk factors for HZ hospitalization included age over 65, obesity (BMI > 30), and poor glycemic control (HbA1c > 8.0 %). These patients had a 40 % increased rehospitalization risk and a 25 % higher risk of severe complications, such as stroke and myocardial infarction, post-HZ.

CONCLUSIONS

Diabetes markedly increases HZ hospitalization rates, rehospitalization, and complication risks. These findings underscore the need for preventive strategies, especially improved glycemic control among high-risk diabetic patients, to inform public health policies and clinical practices aimed at mitigating HZ's impact on this population.

Medical education on hospital hyperglycemia improving knowledge and outcomes

Objective

To evaluate the effects of medical education on hospital hyperglycemia on physician's technical knowledge and the quality of medical prescriptions, patient care, and clinical outcomes.

Subjects and methods

The intervention included online classes and practical consultations provided by an endocrinologist to medical preceptors and residents of the Department of Internal Medicine. A pretest and a post-test (0 to 10 points) were applied before and after the intervention and patients medical records were reviewed before and after the intervention. The outcomes were improvement in medical knowledge, in the quality of prescriptions for patients in the clinical area, and clinical outcomes.

Results

The global mean of correct answers improved with the intervention [before: 6.9 points (±1.7) versus after the intervention: 8.8 points (±1.5) (p < 0.001)]. The number of patients who did not have at least one blood glucose assessment during the entire hospitalization for acute illness decreased from 12.6% before to 2.6% (p < 0.001) after the intervention. There was also a significant reduction in hospital hypoglycemia rates (p < 0.026). The use of sliding-scale insulin as the main treatment was quite low before and after the intervention (2.2% and 0%). After 6 months, medical knowledge did not show significant reduction.

Conclusion

Medical education on hospital hyperglycemia can improve medical knowledge and clinical outcomes for patients. The improvement in medical knowledge was maintained after 6 months.

A Pilot Trial of Continuous Glucose Monitoring Upon Emergency Department Discharge Among People With Diabetes Mellitus

OBJECTIVE

People with diabetes mellitus, particularly those with limited access to longitudinal care, frequently present to the emergency department (ED). Continuous glucose monitoring (CGM) has been shown to improve outcomes in ambulatory settings, so we hypothesized that it would be beneficial if initiated upon ED discharge.

METHODS

We randomized adults with diabetes who were seen in the ED for hypo- or hyperglycemia to either 14 days of flash CGM or care coordination alone. All participants were scheduled to follow up in our diabetes specialty clinic. Outcomes included clinic attendance, the 3-month change in hemoglobin A1c, and repeat ED utilization.

RESULTS

We recruited 30 participants, including 13 with newly diagnosed diabetes. All but one (97%) had type 2 diabetes. We found no significant difference between the CGM (n = 16) and control (n = 14) groups in terms of clinic attendance (75 vs 64%, P = .61) or repeat ED utilization (31 vs 50%, P = .35), although our power was low. The absolute reduction in A1c was greater in the CGM group (5.2 vs 2.4%, P = .08). Among newly diagnosed participants for whom we had data, 7 out of 7 in the CGM group had a follow-up A1c under 7% compared to 1 out of 3 in the control group (P = .03). Over 90% of patients and providers found the CGM useful.

CONCLUSIONS

Our data demonstrate the feasibility of starting CGM in the ED, a valuable setting for engaging difficult-to-reach patients. Our pilot study was limited by its small sample size, however, as recruitment in the ED can be challenging.

Effect of Exercise Therapy on Incident Admission in Patients with Type 2Diabetes Mellitus Undergoing Inpatient Diabetes Self-manageme ntEducation and Support

BACKGROUND

Exercise therapy is the key to preventing admission of patients with type 2 diabetes mellitus (T2DM). However, a few studies have examined the effects of exercise therapy on patients with T2DM undergoing inpatient diabetes self-management education and support (IDSMES).

OBJECTIVE

This study investigated whether exercise therapy influenced the incidence of admission after discharge in patients with T2DM undergoing IDSMES.

METHODS

This retrospective cohort study included patients with T2DM who underwent IDSMES between June 2011 and May 2015. Overall, 258 patients were included in this study. The exercise therapy program was implemented in June 2013. Accordingly, patients diagnosed between June 2011 and May 2013 were categorized as the non-exercise therapy program group, while those diagnosed between June 2013 and May 2015 were categorized as the exercise therapy program group. Outcomes were incident diabetes-related and all-cause admissions within 1 year of discharge. Multiple logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) of the exercise therapy program's impact on the outcomes.

RESULTS

Within 1 year of discharge, 27 (10.5%) patients underwent diabetes-related admissions and 62 (24.0%) underwent all-cause admissions. Multiple logistic regression analyses showed a significant association of the exercise therapy program with incident diabetes-related and allcause admissions [OR: 0.22 (95% CI: 0.08-0.59) and 0.44 (95% CI: 0.22-0.86), respectively].

CONCLUSION

Exercise therapy programs significantly lowered the incidences of diabetes-related and all-cause admissions. This indicates that implementing exercise therapy during hospitalization may be important for preventing admissions of patients with T2DM receiving IDSMES.

American Diabetes Association Professional Practice Committee, ElSayed NA, Aleppo G, Bannuru RR, Bruemmer D, Collins BS, Ekhlaspour L, Galindo RJ, Hilliard ME, Johnson EL, Khunti K, Lingvay I, Matfin G, McCoy RG, Perry ML, Pilla SJ, Polsky S, Prahalad P, Pratley RE, Segal AR, Seley JJ, Stanton RC, Gabbay RA. 16. Diabetes Care in the Hospital: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Clinical and biochemical determinants of length of stay, readmission and recurrence in patients admitted with diabetic ketoacidosis

BACKGROUND

The increasing prevalence of diabetic ketoacidosis (DKA) related admissions poses a significant burden on the healthcare systems globally. However, data regarding the predictors of healthcare resource utilization in DKA is limited and inconsistent. This study aimed to identify key predictors of hospital length of stay (LOS), readmission and recurrent DKA episodes.

METHODS

We undertook a retrospective cross-sectional analysis of all DKA admissions from 2015 to 2021 across four hospitals in Qatar. The primary outcomes were the length of stay (LOS), 90-day readmission and 6-month and 1-year DKA recurrence.

RESULTS

We included 922 patients with a median age of 35 years (25-45). 62% were males with type-1 diabetes-mellitus (T1DM) and type-2 DM (T2DM), present in 52% and 48% of patients. The median LOS was 2.6 days (IQR 1.1-4.8), and the median DKA resolution time was 18 h (10.5-29). Male-gender, new-onset DM, higher Charlson Comorbidity Index (CCI), lower haemoglobin, sodium and potassium, higher urea, longer DKA duration and MICU admission predicted a longer LOS in a multivariate regression analysis. None of the factors were significantly associated with 90-day readmission. Patients with pre-existing T1DM were more likely to have a six-month DKA recurrence than pre-existing T2DM. Patients with a 6-month DKA recurrence, female gender and T1DM had higher odds of 12-month recurrence, whereas a consult with a diabetes educator at the index admission was associated with decreased odds of recurrence.

CONCLUSIONS/INTERPRETATION

This is the most extensive study from the Middle-East region reporting on LOS, readmissions and the recurrence of DKA. Results from this study with a diverse population may be valuable for physicians and healthcare systems to decrease the diabetes-related healthcare burden in DKA patients.

Unscheduled emergency department presentations with diabetes: Identifying high risk characteristics

BACKGROUND

Unscheduled emergency department (ED) presentation by patients with diabetes has seldom been examined. This study aimed to determine the frequency and associated characteristics of presentations in this population.

METHODS

Using a prospective cross-sectional design, data were collected from patients with diabetes presenting and/or admitted to a tertiary metropolitan hospital in New South Wales, Australia (December 2016-September 2017). A screening interview including brief measures of cognitive and executive function, and clinical details from healthcare records were utilised; details around unscheduled presentations within 90 days were extracted. Independent associations with ED presentation were determined.

RESULTS

Unscheduled ED presentations were common; 35.4% had at least one within 90 days, and for 20.1% this occurred within 28 days. The screening tool contributed little towards identifying risk of unscheduled presentation. Those attending any community or outpatient follow-up appointment within the first 28 (OR 0.42, 95% CI 0.23-0.76; p = 0.004) or 90 days (OR 0.25; 0.13-0.47; p < 0.001) from the index presentation were less likely to present within that same period.

CONCLUSIONS

Findings indicated the magnitude of unscheduled ED presentation, care complexity and the value of targeted and timely follow-up. Alternative service support may help maintain and improve diabetes self-management and will require effectiveness and cost-effectiveness evaluation.

Deep Learning vs Traditional Models for Predicting Hospital Readmission among Patients with Diabetes

A hospital readmission risk prediction tool for patients with diabetes based on electronic health record (EHR) data is needed. The optimal modeling approach, however, is unclear. In 2,836,569 encounters of 36,641 diabetes patients, deep learning (DL) long short-term memory (LSTM) models predicting unplanned, all-cause, 30-day readmission were developed and compared to several traditional models. Models used EHR data defined by a Common Data Model. The LSTM model Area Under the Receiver Operating Characteristic Curve (AUROC) was significantly greater than that of the next best traditional model [LSTM 0.79 vs Random Forest (RF) 0.72, p<0.0001]. Experiments showed that performance of the LSTM models increased as prior encounter number increased up to 30 encounters. An LSTM model with 16 selected laboratory tests yielded equivalent performance to a model with all 981 laboratory tests. This new DL model may provide the basis for a more useful readmission risk prediction tool for diabetes patients.

Hospital Readmission Risk and Risk Factors of People with a Primary or Secondary Discharge Diagnosis of Diabetes

Hospital readmission among people with diabetes is common and costly. A better understanding of the differences between people requiring hospitalization primarily for diabetes (primary discharge diagnosis, 1°DCDx) or another condition (secondary discharge diagnosis, 2°DCDx) may translate into more effective ways to prevent readmissions. This retrospective cohort study compared readmission risk and risk factors between 8054 hospitalized adults with a 1°DCDx or 2°DCDx. The primary outcome was all-cause hospital readmission within 30 days of discharge. The readmission rate was higher in patients with a 1°DCDx than in patients with a 2°DCDx (22.2% vs. 16.2%, p < 0.01). Several independent risk factors for readmission were common to both groups including outpatient follow up, length of stay, employment status, anemia, and lack of insurance. C-statistics for the multivariable models of readmission were not significantly different (0.837 vs. 0.822, p = 0.15). Readmission risk of people with a 1°DCDx was higher than that of people with a 2°DCDx of diabetes. Some risk factors were shared between the two groups, while others were unique. Inpatient diabetes consultation may be more effective at lowering readmission risk among people with a 1°DCDx. These models may perform well to predict readmission risk.

16. Diabetes Care in the Hospital: Standards of Care in Diabetes-2023

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Effect of Algoplaque Hydrocolloid Dressing Combined with Nanosilver Antibacterial Gel under Predictive Nursing in the Treatment of Medical Device-Related Pressure Injury

It was aimed at the clinical value of predictive nursing and Algoplaque hydrocolloid dressing (AHD) combined with nanosilver antibacterial gel in treating medical device-related pressure injury (MDRPI). 100 patients, who underwent surgery in Chongqing Qijiang District People's Hospital from February 2019 to February 2020, were selected as the research objects and were randomly divided into the experimental group (50 cases) and the control group (50 cases). For the characterization test, a nanosilver antibacterial gel was created first. Patients in both groups received predictive nursing, but those in the experimental group received AHD and nanosilver antibacterial gel, and those in the control group received gauzes. MDRPI incidence, pressed skin injury severity, comfort level, clothing changes, nursing satisfaction, and other factors were all compared. The particle size of the nanosilver gel was 45-85 nm, with a relatively homogeneous distribution with the medium size, according to the findings. The incidence of MDRPI in the experimental group was lower than that in the control group significantly (6% vs. 30%, P < 0.05). The degree of injury of pressured skin in the experimental group was milder than that in the control group (P < 0.05), the degree of comfort and nursing satisfaction was higher in the experimental group than in the control group (P < 0.05), and dressing change count was lower than that in the control group (P < 0.05). In the treatment of MDRPI, predictive nursing and AHD using nanosilver antibacterial gel showed high clinical application value.

Stationäre Patienten mit der Nebendiagnose Diabetes mellitus: klinische Relevanz

In deutschen Krankenhäusern werden jährlich etwa 3 Mio. Patienten mit Diabetes stationär behandelt, davon 93 % nicht wegen, sondern mit dieser Erkrankung. In einzelnen Fachabteilungen liegt bei bis zu 40 % der Patienten die Nebendiagnose Diabetes vor. Sie haben oft eine relevante Komorbidität und im Vergleich zu Krankenhauspatienten ohne Diabetes eine längere stationäre Verweildauer, entwickeln deutlich häufiger Komplikationen und müssen öfter kurzfristig wieder aufgenommen werden. In dieser Übersicht wird die klinische Relevanz der Nebendiagnose Diabetes mellitus für Krankenhauspatienten besprochen.

The Diabetes Transition of Hospital Care (DiaTOHC) Pilot Study: A Randomized Controlled Trial of an Intervention Designed to Reduce Readmission Risk of Adults with Diabetes

Hospital readmission within 30 days of discharge (30-day readmission) is a high-priority quality measure and cost target. The purpose of this study was to explore the feasibility and efficacy of the Diabetes Transition of Hospital Care (DiaTOHC) Program on readmission risk in high-risk adults with diabetes. This was a non-blinded pilot randomized controlled trial (RCT) that compared usual care (UC) to DiaTOHC at a safety-net hospital. The primary outcome was all-cause 30-day readmission. Between 16 October 2017 and 30 May 2019, 93 patients were randomized. In the intention-to-treat (ITT) population, 14 (31.1%) of 45 DiaTOHC subjects and 15 (32.6%) of 46 UC subjects had a 30-day readmission, while 35.6% DiaTOHC and 39.1% UC subjects had a 30-day readmission or ED visit. The Intervention−UC cost ratio was 0.33 (0.13−0.79) 95%CI. At least 93% of subjects were satisfied with key intervention components. Among the 69 subjects with baseline HbA1c >7.0% (53 mmol/mol), 30-day readmission rates were 23.5% (DiaTOHC) and 31.4% (UC) and composite 30-day readmission/ED visit rates were 26.5% (DiaTOHC) and 40.0% (UC). In this subgroup, the Intervention−UC cost ratio was 0.21 (0.08−0.58) 95%CI. The DiaTOHC Program may be feasible and may decrease combined 30-day readmission/ED visit risk as well as healthcare costs among patients with HbA1c levels >7.0% (53 mmol/mol).

Predictors of Rehospitalization and Mortality in Diabetes-Related Hospital Admissions

The risk factors of rehospitalization and death post-discharge in diabetes-related hospital admissions are not fully understood. To determine them, a population-based retrospective epidemiological survey was performed on diabetes-related admissions from the Polish national database. Logistic regression models were used, in which the dependent variables were rehospitalization due to diabetes complications and death within 90 days after the index hospitalization. In 2017, there were 74,248 hospitalizations related to diabetes. A total of 11.3% ended with readmission. Risk factors for rehospitalization were as follows: age < 35 years; male sex; prior hospitalization due to acute diabetic complications; weight loss; peripheral artery disease; iron deficiency anemia; kidney failure; alcohol abuse; heart failure; urgent, emergency, or weekend admission; length of hospitalization; and hospitalization in a teaching hospital with an endocrinology/diabetology unit. Furthermore, 7.3% of hospitalizations resulted in death within 90 days following discharge. Risk factors for death were as follows: age; neoplastic disease with/without metastases; weight loss; coagulopathy; alcohol abuse; acute diabetes complications; heart failure; kidney failure; iron deficiency anemia; peripheral artery disease; fluid, electrolytes, and acid-base balance disturbances; urgent or emergency and weekend admission; and length of hospitalization. We concluded that of all investigated factors, only hospitalization within an experienced specialist center may reduce the frequency of the assessed outcomes.