Rate and Predictors of 30-day Readmission Following Diabetic Ketoacidosis in Type 1 Diabetes Mellitus: A US Analysis

Factors contributing to diabetic ketoacidosis readmission in hospital settings in the United States: A scoping review

BACKGROUND

Hospitalization of patients with DKA creates a significant burden on the US healthcare system. While previous studies have identified multiple potential contributors, a comprehensive review of the factors leading to DKA readmissions within the US healthcare system has not been done. This scoping review aims to identify how access to care, treatment adherence, socioeconomic status, race, and ethnicity impact DKA readmission-related patient morbidity and mortality and contribute to the socioeconomic burden on the US healthcare system. Additionally, this study aims to integrate current recommendations to address this multifactorial issue, ultimately reducing the burden at both individual and organizational levels.

METHODS

The PRISMA-SCR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) was used as a reference checklist throughout this study. The Arksey and O'Malley methodology was used as a framework to guide this review. The framework methodology consisted of five steps: (1) Identify research questions; (2) Search for relevant studies; (3) Selection of studies relevant to the research questions; (4) Chart the data; (5) Collate, summarize, and report the results.

RESULTS

A total of 15 articles were retained for analysis. Among the various social factors identified, those related to sex/gender (n = 9) and age (n = 9) exhibited the highest frequency. Moreover, race and ethnicity (n = 8) was another recurrent factor that appeared in half of the studies. Economic factors were also identified in this study, with patient insurance type having the highest frequency (n = 11). Patient income had the second highest frequency (n = 6). Multiple studies identified a link between patients of a specific race/ethnicity and decreased access to treatment. Insufficient patient education around DKA treatment was noted to impact treatment accessibility. Certain recommendations for future directions were highlighted as recurrent themes across included studies and encompassed patient education, early identification of DKA risk factors, and the need for a multidisciplinary approach using community partners such as social workers and dieticians to decrease DKA readmission rates in diabetic patients.

CONCLUSION

This study can inform future policy decisions to improve the accessibility, affordability, and quality of healthcare through evidence-based interventions for patients with DM following an episode of DKA.

Hyperglycemic Crises in Adults With Diabetes: A Consensus Report

The American Diabetes Association (ADA), European Association for the Study of Diabetes (EASD), Joint British Diabetes Societies for Inpatient Care (JBDS), American Association of Clinical Endocrinology (AACE), and Diabetes Technology Society (DTS) convened a panel of internists and diabetologists to update the ADA consensus statement on hyperglycemic crises in adults with diabetes, published in 2001 and last updated in 2009. The objective of this consensus report is to provide up-to-date knowledge about the epidemiology, pathophysiology, clinical presentation, and recommendations for the diagnosis, treatment, and prevention of diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar state (HHS) in adults. A systematic examination of publications since 2009 informed new recommendations. The target audience is the full spectrum of diabetes health care professionals and individuals with diabetes.

Hyperglycaemic crises in adults with diabetes: a consensus report

The American Diabetes Association (ADA), European Association for the Study of Diabetes (EASD), Joint British Diabetes Societies for Inpatient Care (JBDS), American Association of Clinical Endocrinology (AACE) and Diabetes Technology Society (DTS) convened a panel of internists and diabetologists to update the ADA consensus statement on hyperglycaemic crises in adults with diabetes, published in 2001 and last updated in 2009. The objective of this consensus report is to provide up-to-date knowledge about the epidemiology, pathophysiology, clinical presentation, and recommendations for the diagnosis, treatment and prevention of diabetic ketoacidosis (DKA) and hyperglycaemic hyperosmolar state (HHS) in adults. A systematic examination of publications since 2009 informed new recommendations. The target audience is the full spectrum of diabetes healthcare professionals and individuals with diabetes.

A Systematic Review of Recent Studies on Hospital Readmissions of Patients With Diabetes

Hospital readmissions are a major area of concern across the healthcare ecosystem. Diabetes mellitus (DM) and associated complications significantly contributed to hospital readmissions in 2018, placing it among the leading causes alongside septicemia and heart failure. Diabetes is an urgent public health concern that has reached epidemic proportions globally. Compared to the early 2000s, the prevalence of diabetes among individuals aged 20-79 years in the US has significantly increased. This research provides an in-depth examination of diabetes-related hospital readmissions and reviews recent studies (2015-2023) to understand the characteristics, risk factors, and potential outcomes for re-admitted diabetes patients. The study identified 21 articles that met the inclusion criteria to provide valuable insights and analyze risk factors associated with these readmissions. The findings indicated that risk factors such as age, demographics, income, insurance type, severity of illness, and comorbidities among diabetic patients were critical and warranted further investigation. Diabetes awareness, quality of hospital care, involvement of healthcare providers, timely screening, and lifestyle changes were noted as important factors to improve the effectiveness of healthcare delivery, reduce diabetes-related complications, and eventually lower preventable hospital readmissions.

Feature selection and risk prediction for diabetic patients with ketoacidosis based on MIMIC-IV

Background

Diabetic ketoacidosis (DKA) is a frequent acute complication of diabetes mellitus (DM). It develops quickly, produces severe symptoms, and greatly affects the lives and health of individuals with DM.This article utilizes machine learning methods to examine the baseline characteristics that significantly contribute to the development of DKA. Its goal is to identify and prevent DKA in a targeted and early manner.

Methods

This study selected 2382 eligible diabetic patients from the MIMIC-IV dataset, including 1193 DM patients with ketoacidosis and 1186 DM patients without ketoacidosis. A total of 42 baseline characteristics were included in this research. The research process was as follows: Firstly, important features were selected through Pearson correlation analysis and random forest to identify the relevant physiological indicators associated with DKA. Next, logistic regression was used to individually predict DKA based on the 42 baseline characteristics, analyzing the impact of different physiological indicators on the experimental results. Finally, the prediction of ketoacidosis was performed by combining feature selection with machine learning models include logistic regression, XGBoost, decision tree, random forest, support vector machine, and k-nearest neighbors classifier.

Results

Based on the importance analysis conducted using different feature selection methods, the top five features in terms of importance were identified as mean hematocrit (haematocrit_mean), mean hemoglobin (haemoglobin_mean), mean anion gap (aniongap_mean), age, and Charlson comorbidity index (charlson_comorbidity_index). These features were found to have significant relevance in predicting DKA. In the individual prediction using logistic regression, these five features have been proven to be effective, with F1 scores of 1.000 for hematocrit mean, 0.978 for haemoglobin_mean, 0.747 for age, 0.692 for aniongap_mean and 0.666 for charlson_comorbidity_index. These F1 scores indicate the effectiveness of each feature in predicting DKA, with the highest score achieved by mean hematocrit. In the prediction of DKA using machine learning models, including logistic regression, XGBoost, decision tree, and random forest demonstrated excellent results, achieving an F1 score of 1.000. Additionally, by applying feature selection techniques, noticeable improvements were observed in the experimental performance of the support vector machine and k-nearest neighbors classifier.

Conclusion

The study found that hematocrit, hemoglobin, anion gap, age, and Charlson comorbidity index are closely associated with ketoacidosis. In clinical practice, these five baseline characteristics should be given with the special attention to achieve early detection and treatment, thus reducing the incidence of the disease.

Hospitalizations and Mortality From Myasthenia Gravis: Trends From 2 US National Datasets

BACKGROUND AND OBJECTIVES

Myasthenia gravis (MG) is a rare neuromuscular disorder where IgG antibodies damage the communication between nerves and muscles, leading to muscle weakness that can be severe and have a significant impact on patients' lives. MG exacerbations include myasthenic crisis with respiratory failure, the most serious manifestation of MG. Recent studies have found MG prevalence increasing, especially in older patients. This study examined trends in hospital admissions and in-hospital mortality for adult patients with MG and readmissions and postdischarge mortality in older (65 years or older) adults with MG.

METHODS

Data from the Nationwide Inpatient Sample (NIS), an all-payer national database of hospital discharges, were used to characterize trends in hospitalizations and in-hospital mortality related to MG exacerbations and MG crisis among adult patients aged 18 years or older. The Medicare Limited Data Set, a deidentified, longitudinal research database with demographic, enrollment, and claims data was used to assess hospitalizations, length of stay (LOS), readmissions, and 30-day postdischarge mortality among fee-for-service Medicare beneficiaries aged 65 years or older. The study period was 2010-2019. Multinomial logit models and Poisson regression were used to test for significance of trends.

RESULTS

Hospitalization rates for 19,715 unique adult patients and 56,822 admissions increased from 2010 to 2019 at an average annualized rate of 4.9% (MG noncrisis: 4.4%; MG crisis: 6.8%; all p < 0.001). Readmission rates were approximately 20% in each study year for both crisis and noncrisis hospitalizations; the in-hospital mortality rate averaged 1.8%. Among patients aged 65 years or older, annualized increases in hospitalizations were estimated at 5.2%, 4.2%, and 7.7% for all, noncrisis, and crisis hospitalizations, respectively (all p < 0.001). The average LOS was stable over the study period, ranging from 11.3 to 13.1 days, but was consistently longer for MG crisis admissions. Mortality among patients aged 65 years or older was higher compared with that in all patients, averaging 5.0% across each of the study years.

DISCUSSION

Increasing hospitalization rates suggest a growing burden associated with MG, especially among older adults. While readmission and mortality rates have remained stable, the increasing hospitalization rates indicate that the raw numbers of readmissions-and deaths-are also increasing. Mortality rates are considerably higher in older patients hospitalized with MG.

Prediction of Early and Long-Term Hospital Readmission in Patients with Severe Obesity: A Retrospective Cohort Study

Adults with obesity have a higher risk of hospitalization and high hospitalization-related healthcare costs. However, a predictive model for the risk of readmission in patients with severe obesity is lacking. We conducted a retrospective cohort study enrolling all patients admitted for severe obesity (BMI ≥ 40 kg/m2) between 2009 and 2018 to the Istituto Auxologico Italiano in Piancavallo. For each patient, all subsequent hospitalizations were identified from the regional database by a deterministic record-linkage procedure. A total of 1136 patients were enrolled and followed up for a median of 5.7 years (IQR: 3.1-8.2). The predictive factors associated with hospital readmission were age (HR = 1.02, 95%CI: 1.01-1.03, p < 0.001), BMI (HR = 1.02, 95%CI: 1.01-1.03, p = 0.001), smoking habit (HR = 1.17, 95%CI: 0.99-1.38, p = 0.060), serum creatinine (HR = 1.22, 95%CI: 1.04-1.44, p = 0.016), diabetes (HR = 1.17, 95%CI: 1.00-1.36, p = 0.045), and number of admissions in the previous two years (HR = 1.15, 95%CI: 1.07-1.23, p < 0.001). BMI lost its predictive role when restricting the analysis to readmissions within 90 days. BMI and diabetes lost their predictive roles when further restricting the analysis to readmissions within 30 days. In conclusion, in this study, we identified predictive variables associated with early and long-term hospital readmission in patients with severe obesity. Whether addressing modifiable risk factors could improve the outcome remains to be established.

Mortality prediction in patients with hyperglycaemic crisis using explainable machine learning: a prospective, multicentre study based on tertiary hospitals

BACKGROUND

Experiencing a hyperglycaemic crisis is associated with a short- and long-term increased risk of mortality. We aimed to develop an explainable machine learning model for predicting 3-year mortality and providing individualized risk factor assessment of patients with hyperglycaemic crisis after admission.

METHODS

Based on five representative machine learning algorithms, we trained prediction models on data from patients with hyperglycaemic crisis admitted to two tertiary hospitals between 2016 and 2020. The models were internally validated by tenfold cross-validation and externally validated using previously unseen data from two other tertiary hospitals. A SHapley Additive exPlanations algorithm was used to interpret the predictions of the best performing model, and the relative importance of the features in the model was compared with the traditional statistical test results.

RESULTS

A total of 337 patients with hyperglycaemic crisis were enrolled in the study, 3-year mortality was 13.6% (46 patients). 257 patients were used to train the models, and 80 patients were used for model validation. The Light Gradient Boosting Machine model performed best across testing cohorts (area under the ROC curve 0.89 [95% CI 0.77-0.97]). Advanced age, higher blood glucose and blood urea nitrogen were the three most important predictors for increased mortality.

CONCLUSION

The developed explainable model can provide estimates of the mortality and visual contribution of the features to the prediction for an individual patient with hyperglycaemic crisis. Advanced age, metabolic disorders, and impaired renal and cardiac function were important factors that predicted non-survival.

TRIAL REGISTRATION NUMBER

ChiCTR1800015981, 2018/05/04.

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.

Predicting readmission due to severe hyperglycemia after a hyperglycemic crisis episode

AIM

This study aimed to investigate the readmission pattern and risk factors for patients who experienced a hyperglycemic crisis.

METHODS

Patients admitted to MacKay Memorial Hospital for diabetic ketoacidosis (DKA) or hyperglycemic hyperosmolar state (HHS) between January 2016 and April 2019 were studied. The timing of the first readmission for hyperglycemia and other causes was recorded. Kaplan-Meier analysis was used to compare patients with hyperglycemia and all-cause readmissions. Cox regression was used to identify independent predictors for hyperglycemia and all-cause readmission post-discharge.

RESULTS

The study cohort included 410 patients, and 15.3 % and 46.3 % of them had hyperglycemia and all-cause readmissions, respectively. The DKA and HHS group showed a similar incidence for hyperglycemia, with the latter group showing a higher incidence of all-cause readmissions. The significant predictors of hyperglycemia readmissions included young age, smoking, hypoglycemia, higher effective osmolality, and hyperthyroidism in the DKA group and higher glycated hemoglobin level in the HHS group.

CONCLUSIONS

Patients who experienced DKA and HHS had similar hyperglycemia readmission rates; however, predictors in the DKA group were not applicable to the HHS group. Designing different strategies for different types of hyperglycemic crisis is necessary for preventing readmission.

Rising Readmission Rates After Diabetic Ketoacidosis Hospitalization Among Adults With Type 1 Diabetes Throughout a Decade in the United States

Research on longitudinal trends in readmission rates after diabetic ketoacidosis (DKA) is lacking. This retrospective study was aimed at identifying trends in readmissions after hospitalization for DKA, as well as trends in outcomes after readmission, over time among adults with type 1 diabetes in the United States. Findings indicate that the DKA readmission rate increased from 53 to 73 events per 100,000 between 2010 to 2018, and low-income and uninsured patients had higher odds of readmission. There was no significant change in mortality after readmission over time. Improved access to care and affordable management options may play a crucial role in preventing readmissions.

Management of hospitalized patients with mild to moderate diabetic ketoacidosis using a continuous insulin infusion protocol on a medical surgical ward and observation level of care: A retrospective cohort study

Although the practice of using rapid-acting subcutaneous insulin for the management of mild-to-moderate diabetic ketoacidosis is becoming increasingly popular, the continuous insulin infusion remains widely utilized, and its real-world applicability and safety on a medical surgical unit (Med Surg) and observation level of care are unclear. We assessed whether a continuous insulin infusion protocol for mild-to-moderate diabetic ketoacidosis on Med Surg/observation level of care over a 6.5-year period was associated with adverse outcomes. A retrospective cohort study of adults hospitalized with mild-to-moderate diabetic ketoacidosis was conducted at 2 community hospitals in Northern California, USA, from January 2014 to May 2020. Demographic and clinical variables were collected using an electronic health record. Admission to Med Surg/observation was compared to intensive care unit admission for the outcomes of 30-day readmission, presence of hypoglycemia, rate of hypoglycemic episodes, in-hospital and 30-day mortality, and length of stay using bivariate analysis. Among 227 hospital encounters (mean age 41 years, 52.9% women, 79.3% type 1 diabetes, 97.4% utilization of continuous insulin infusion), 19.4% were readmitted within 30 days, and 20.7% developed hypoglycemia. For Med Surg/observation encounters compared to the intensive care unit, there were no statistically significant differences in the risk of readmission (RR 1.48, 95% CI, 0.86-2.52), hypoglycemia (RR 1.17, 95% CI, 0.70-1.95), or increased length of stay (RR 0.71, 95% CI, 0.55-1.02); there was a lower risk of hypoglycemic events during hospitalization (RR 0.69, 95% CI, 0.54-0.96). Continuous insulin infusion utilization may be a safe option for treatment of mild-to-moderate diabetic ketoacidosis on Med Surg/observation level of care. Further investigation is needed.

Comorbidities in Recent-Onset Adult Type 1 Diabetes: A Comparison of German Cohorts

AIMS

Restrictive exclusion criteria from different study populations may limit the generalizability of the observations. By comparing two differently designed German cohorts, we assessed the prevalence of cardiovascular risk factors and diabetes-related complications in recent-onset adult type 1 diabetes.

METHODS

This study evaluated 1511 persons with type 1 diabetes of the prospective diabetes follow-up registry (DPV) and 268 volunteers of the prospective observational German Diabetes Study (GDS) with a known diabetes duration <1 year. Participants had similar age (36 years), sex distribution (41% female) and BMI (26 kg/m²) in both cohorts.

RESULTS

The average HbA1c was 6.4 ± 0.8% in the GDS and 7.0 ± 1.1% in the DPV. Prevalence of hypertension (24%) was similar, while more DPV participants had dyslipidemia and lipid-lowering medication than GDS participants (77% vs. 41% and 7% vs. 2%, respectively; p<0.05). Prevalence of retinopathy and nephropathy was higher in DPV compared to GDS participants (10% vs. 3% and 18% vs. 7%, respectively; p<0.001).

CONCLUSIONS

Diabetic nephropathy and retinopathy are the most frequent complications in type 1 diabetes, affecting up to every 10th patient within the first year after diagnosis, underlining the need for more stringent risk factor management already at the time of diagnosis of type 1 diabetes.

A Revised Comorbidity Model for Administrative Databases Using Clinical Classifications Software Refined Variables

Background and objective

Database research has shaped policies, identified trends, and informed healthcare guidelines for numerous disease conditions. However, despite their abundant uses and vast potential, administrative databases have several limitations. Adjusting outcomes for comorbidities is often needed during database analysis as a means of overcoming non-randomization. We sought to obtain a model for comorbidity adjustment based on Clinical Classifications Software Refined (CCSR) variables and compare this with current models. Our aim was to provide a simplified, adaptable, and accurate measure for comorbidities in the Agency for Healthcare Research and Quality (AHRQ) databases, in order to strengthen the validity of outcomes. 

Methods

The Nationwide Inpatient Sample (NIS) database for 2018 was the data source. We obtained the mortality rate among all included hospitalizations in the dataset. A model based on CCSR categories was mapped from disease groups in Sundararajan's adaptation of the modified Deyo’s Charlson Comorbidity Index (CCI). We employed logistic regression analysis to obtain the final model using CCSR variables as binary variables. We tested the final model on the 10 most common reasons for hospitalizations.

Results

The model had a higher area under the curve (AUC) compared to the three modalities of the CCI studied in all the categories. Also, the model had a higher AUC compared to the Elixhauser model in 8/10 categories. However, the model did not have a higher AUC compared to a model made from stepwise backward regression analysis of the original 21-variable model.

Conclusion

We developed a 15-CCSR-variable model that showed good discrimination for inpatient mortality compared to prior models.

Risk Factors, Trends, and Preventive Measures for 30-Day Unplanned Diabetic Ketoacidosis Readmissions in the Pediatric Population

Background There has been a steady rise in types 1 and 2 diabetes mellitus among the youth in the USA from 2001 to 2017. Diabetic ketoacidosis (DKA) is a common and preventable presentation of both types of diabetes mellitus. According to the Centers for Disease Control and Prevention's (CDC) United States Diabetes Surveillance System, during 2004-2019 an increase in DKA hospitalization rates by 59.4% was noted, with people aged less than 45 years having the highest rates. Readmissions reflect the quality of disease management, which is integrally tied to care coordination and communication with the patient and their families. This study analyzes the trends and risk factors contributing to 30-day unplanned DKA readmissions in the pediatric age group and looks into possible preventive measures to decrease them. Methods A retrospective study was performed using the National Readmission Database (NRD) from January 1, 2017, to December 1, 2017. Pediatric patients aged 18 years and younger with the primary diagnosis of DKA were included using the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code E10.10. All statistical analysis was performed using IBM SPSS Statistics for Windows, version 1.0.0.1327 (IBM Corp., Armonk, NY, USA). Pearson's chi-square test was used for categorical variables and Mann-Whitney U test was used for continuous variables. To independently determine the predictors of readmission within each clinical variable, multiple logistic regressions with values presented as odds ratios (OR) with 95% confidence intervals (CI) were performed. Results A weighted total of 19,519 DKA-related pediatric index admissions were identified from the 2017 NRD. Of these pediatric patients, 831 (4.3%) had 30-day DKA readmission. The median age of a child for readmission was 16 years with an interquartile range of 0 to 18 years. A sharp rise in 30-day DKA readmissions was noted for ages 16 years and over. Females in the 0-25th percentile median household income category, with Medicaid covered, large metropolitan areas with at least 1 million residents, and metropolitan teaching hospitals were found to have a statistically significant higher percentage of readmissions. The mean length of stay for those who had a DKA readmission was 2.06 days, with a standard deviation of 1.84 days. The mean hospital charges for those who had a DKA readmission were $ 20,339.70. The 30-day DKA readmission odds were seen to be increased for female patients, Medicaid-insured patients, admissions at metropolitan non-teaching hospitals, and children from 0-25th percentile median household income category. Conclusion There has not been much of a change in the trend and risk factors contributing to the 30-day unplanned DKA readmissions over the years despite the steady rise in cases of diabetes mellitus. The length of stay for those who did not get readmitted within 30 days was longer than for those who did. This could reflect more comprehensive care and discharge planning that may have prevented them from readmission. Diabetes mellitus is a chronic disease that demands a team effort from the patient, family, healthcare personnel, insurance companies, and lawmakers. There is scope for a lot of improvement with the way our patients are being managed, and a more holistic approach needs to be devised.