PREDICTORS FOR ADVERSE OUTCOMES IN DIABETIC KETOACIDOSIS IN A MULTIHOSPITAL HEALTH SYSTEM

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.

Development of Machine Learning Models for the Identification of Elevated Ketone Bodies During Hyperglycemia in Patients with Type 1 Diabetes

Aims: Diabetic ketoacidosis (DKA) is a serious life-threatening condition caused by a lack of insulin, which leads to elevated plasma glucose and metabolic acidosis. Early identification of developing DKA is important to start treatment and minimize complications and risk of death. The aim of the present study is to develop and test prediction model(s) that gives an alarm about their risk of developing elevated ketone bodies during hyperglycemia. Methods: We analyzed data from 138 type 1 diabetes patients with measurements of ketone bodies and continuous glucose monitoring (CGM) data from over 30,000 days of wear time. We utilized a supervised binary classification machine learning approach to identify elevated levels of ketone bodies (≥0.6 mmol/L). Data material was randomly divided at patient level in 70%/30% (training/test) dataset. Logistic regression (LR) and random forest (RF) classifier were compared. Results: Among included patients, 913 ketone samples were eligible for modeling, including 273 event samples with ketone levels ≥0.6 mmol/L. An area under the receiver operating characteristic curve from the RF classifier was 0.836 (confidence interval [CI] 90%, 0.783-0.886) and 0.710 (CI 90%, 0.646-0.77) for the LR classifier. Conclusions: The novel approach for identifying elevated ketone levels in patients with type 1 diabetes utilized in this study indicates that CGM could be a valuable resource for the early prediction of patients at risk of developing DKA. Future studies are needed to validate the results.

Differential evolution of diabetic ketoacidosis in adults with pre-existent versus newly diagnosed type 1 and type 2 diabetes mellitus

BACKGROUND

Diabetic ketoacidosis (DKA) was once known to be specific to type-1 diabetes-mellitus (T1D); however, many cases are now seen in patients with type-2 diabetes-mellitus (T2D). Little is known about how this etiology shift affects DKA's outcomes.

METHODS

We studied consecutive index DKA admissions from January 2015 to March 2021. Descriptive analyses were performed based on pre-existing T1D and T2D (PT1D and PT2D, respectively) and newly diagnosed T1D and T2D (NT1D and NT2D, respectively).

RESULTS

Of the 922 patients, 480 (52%) had T1D, of which 69% had PT1D and 31% NT1D, whereas 442 (48%) had T2D, of which 60% had PT2D and 40% NT2D. The mean age was highest in PT2D (47.6 ± 13.1 years) and lowest in PT1D (27.3 ± 0.5 years) (P < 0.001). Patients in all groups were predominantly male except in the PT1D group (55% females) (P < 0.001). Most patients were Arabic (76% in PT1D, 51.4% in NT1D, 46.6% in PT2D) except for NT2D, which mainly comprised Asians (53%) (P < 0.001). Patients with NT2D had the longest hospital length of stay (LOS) (6.8 ± 11.3 days) (P < 0.001), longest DKA duration (26.6 ± 21.1 h) (P < 0.001), and more intensive-care unit (ICU) admissions (31.2%) (P < 0.001). Patients with PT1D had the shortest LOS (2.5 ± 3.5 days) (P < 0.001), DKA duration (18.9 ± 4.2 h) (P < 0.001), and lowest ICU admissions (16.6%) (P < 0.001).

CONCLUSIONS/INTERPRETATION

We presented the largest regional data on differences in DKA based on the type and duration of diabetes- mellitus (DM), showing that T2D is becoming an increasing cause of DKA, with worse clinical outcomes (especially newly diagnosed T2D) compared to T1D.

Base excess measured at hospital admission is useful for predicting diabetic ketoacidosis severity and resolution time in adult patients

BACKGROUND AND AIMS

This study aimed to identify the biochemical factors measured at hospital admission that could predict diabetes ketoacidosis (DKA) resolution time in adult patients.

MATERIALS AND METHODS

This retrospective study included 79 patients >18 years of age. Multivariate analyses were performed to determine which variables might predict DKA resolution time. Biochemical parameters between the two DKA resolution time groups were compared.

RESULTS

Using multiple linear regression models, acidosis time was found to decrease by 29 h if the pH value increased by one unit, 0.64 h if the base excess (BE) value increased by 1 mmol, and 1.09 h if the bicarbonate (HCO3^-) value increased by 1 mmol. The biochemical parameters that differed between the two groups were pH, HCO3^-, and BE. Patients with delayed resolution of DKA had a blood pH of 7.1 (±0.18), HCO3^- of 5.1 mmol (2.9-11.6 mmol), and BE of -21.5 mmol (-28.2 to -14.4 mmol) at hospital admission.

CONCLUSIONS

Lower pH, HCO3^-, and BE values at hospital admission may predict longer DKA resolution times in adult patients. In addition, BE may predict DKA severity.

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

PURPOSE OF REVIEW

Acute care re-utilization, i.e., hospital readmission and post-discharge Emergency Department (ED) use, is a significant driver of healthcare costs and a marker for healthcare quality. Diabetes is a major contributor to acute care re-utilization and associated costs. The goals of this paper are to (1) review the epidemiology of readmissions among patients with diabetes, (2) describe models that predict readmission risk, and (3) address various strategies for reducing the risk of acute care re-utilization.

RECENT FINDINGS

Hospital readmissions and ED visits by diabetes patients are common and costly. Major risk factors for readmission include sociodemographics, comorbidities, insulin use, hospital length of stay (LOS), and history of readmissions, most of which are non-modifiable. Several models for predicting the risk of readmission among diabetes patients have been developed, two of which have reasonable accuracy in external validation. In retrospective studies and mostly small randomized controlled trials (RCTs), interventions such as inpatient diabetes education, inpatient diabetes management services, transition of care support, and outpatient follow-up are generally associated with a reduction in the risk of acute care re-utilization. Data on readmission risk and readmission risk reduction interventions are limited or lacking among patients with diabetes hospitalized for COVID-19. The evidence supporting post-discharge follow-up by telephone is equivocal and also limited. Acute care re-utilization of patients with diabetes presents an important opportunity to improve healthcare quality and reduce costs. Currently available predictive models are useful for identifying higher risk patients but could be improved. Machine learning models, which are becoming more common, have the potential to generate more accurate acute care re-utilization risk predictions. Tools embedded in electronic health record systems are needed to translate readmission risk prediction models into clinical practice. Several risk reduction interventions hold promise but require testing in multi-site RCTs to prove their generalizability, scalability, and effectiveness.