Identifying patients at high risk for diabetic ketoacidosis (DKA) is crucial for informing efforts at preventive intervention. This study sought to develop and validate an electronic medical record (EMR)-based tool for predicting DKA risk in pediatric patients with type 1 diabetes. Based on analysis of data from 1,864 patients with type 1 diabetes, three factors emerged as significant predictors of DKA: most recent A1C, type of health insurance (public vs. private), and prior DKA. A prediction model was developed based on these factors and tested to identify and categorize patients at low, moderate, and high risk for experiencing DKA within the next year. This work demonstrates that risk for DKA can be predicted using a simple model that can be automatically derived from variables in the EMR.

Diabetic ketoacidosis (DKA) is an acute, severe complication of type 1 diabetes that can result in significant morbidity and mortality. It is typically characterized by a triad of hyperglycemia, metabolic acidosis, and ketosis caused by inadequate circulatory insulin levels associated with an increase in counterregulatory hormone levels (1). Data from the T1D Exchange clinic registry suggests that about one in 10 youth with type 1 diabetes in the United States will experience an episode of DKA in a given year (2), and a recent nationwide study showed a 59% increase in DKA-related hospitalizations from 2003 to 2014, with a concomitant increase in health care costs, from $2.2 billion in 2003 to $5.1 billion in 2014 (3).

In established patients with type 1 diabetes, the most frequent cause of DKA is insulin omission, especially in the context of chronic hyperglycemia (4). Poor sick-day management also contributes to DKA, despite the existence of well-established clinical guidelines (5). DKA is therefore most often preventable after new onset. Known risk factors for DKA include elevated A1C, prior episodes of DKA, non-White race, underinsurance, lower household income, adolescent age, female sex, unstable family condition, and underlying mental health issues (68). Crucially, a majority of diabetes-related hospitalizations post-diagnosis are for recurrent DKA (9,10), suggesting that there is a cohort of patients more prone to experience DKA who account for a substantial portion of the morbidity and costs (11). Identifying patients at greatest risk for DKA can inform efforts at preventive intervention and potentially result in substantial improvements in the health of children and youth with diabetes.

Predictive risk models have been implemented by various medical specialties to identify and stratify patients at risk for serious medical problems such as the development of type 2 diabetes (12) and cardiovascular disease events (13). In 2014, our team developed and validated the Risk Index for Poor Glycemic Control (RI-PGC) (14), a nine-item psychosocial assessment tool designed to be administered as a brief structured interview at the time of diabetes diagnosis. The RI-PGC predicts risk for poor glycemic control (defined as mean A1C ≥9.5%) and DKA 2 years post-diagnosis and can be used to inform interventions and follow-up care (14,15).

In addition to identifying risk at diabetes diagnosis, it is important to have a way to provide updated risk estimations for patients with established diabetes, especially as relevant variables (e.g., glycemic control) change over time. However, regularly screening patients for DKA risk can be challenging given the time and staffing resources required. One way to manage this problem would be to create an automated process that could generate a risk estimation that updates over time. This article reports on the development and validation of a new, automated, electronic medical record (EMR)-based risk prediction tool for established patients with type 1 diabetes, the Risk Index for Diabetic Ketoacidosis, or RI-DKA.

Data were extracted on individuals with type 1 diabetes seen through the Endocrine and Diabetes Care Center at a large urban children’s hospital and academic medical center with three regional hospital campuses and seven ambulatory clinics; the cohort was drawn from the population of patients receiving care at any of these sites. We included all patients seen in fiscal years 2016 (FY2016) and 2017 (FY2017) who had an International Classification of Diseases, 9th or 10th revisions, Clinical Modification (ICD-9-CM or ICD-10-CM), diagnosis of type 1 diabetes; a diabetes duration ≥6 months; age ≤19 years; and at least one outpatient clinic visit and at least one A1C test in each fiscal year (FY2016 and FY2017) (Table 1). In FY2016, 1,864 patients met inclusion criteria, and in FY2017, 1,903 patients met criteria; 1,548 patients (81.3%) were included in both cohorts, with 355 new patients included in FY2017.

TABLE 1

Demographic and Clinical Variables for Individuals in FY2016 and FY2017 Cohorts

FY2016
(n = 1,864)
FY2017
(n = 1,903)
Age at end of FY, years 13.4 ± 3.8 13.5 ± 3.8 
Age, years
1–6
7–12
13–19 

134 (7.2)
624 (33.5)
1,106 (59.3) 

129 (6.8)
640 (33.6)
1,134 (59.6) 
Sex
Female
Male 

946 (50.8)
918 (49.3) 

951 (50.0)
952 (50.0) 
Race/ethnicity
White non-Hispanic
Hispanic
Black
Other 

993 (53.3)
460 (24.7)
322 (17.3)
89 (4.8) 

1,004 (52.8)
464 (24.4)
323 (17.0)
112 (5.9) 
Health insurance
Private
Public/self-pay 

1,154 (61.9)
710 (38.1) 

1,163 (61.1)
740 (38.9) 
Last A1C result, % 8.3 (7.4–9.4) 8.4 (7.5–9.6) 
Insulin regimen
IIM
Fixed mealtime dose
Insulin pump
Other 

795 (42.7)
347 (18.6)
706 (37.9)
16 (0.9) 

817 (42.9)
279 (14.7)
793 (41.7)
14 (0.7) 
DKA in prior two FYs
Yes
No 

199 (10.7)
1,665 (89.3) 

127 (6.7)
1,776 (93.3) 
FY2016
(n = 1,864)
FY2017
(n = 1,903)
Age at end of FY, years 13.4 ± 3.8 13.5 ± 3.8 
Age, years
1–6
7–12
13–19 

134 (7.2)
624 (33.5)
1,106 (59.3) 

129 (6.8)
640 (33.6)
1,134 (59.6) 
Sex
Female
Male 

946 (50.8)
918 (49.3) 

951 (50.0)
952 (50.0) 
Race/ethnicity
White non-Hispanic
Hispanic
Black
Other 

993 (53.3)
460 (24.7)
322 (17.3)
89 (4.8) 

1,004 (52.8)
464 (24.4)
323 (17.0)
112 (5.9) 
Health insurance
Private
Public/self-pay 

1,154 (61.9)
710 (38.1) 

1,163 (61.1)
740 (38.9) 
Last A1C result, % 8.3 (7.4–9.4) 8.4 (7.5–9.6) 
Insulin regimen
IIM
Fixed mealtime dose
Insulin pump
Other 

795 (42.7)
347 (18.6)
706 (37.9)
16 (0.9) 

817 (42.9)
279 (14.7)
793 (41.7)
14 (0.7) 
DKA in prior two FYs
Yes
No 

199 (10.7)
1,665 (89.3) 

127 (6.7)
1,776 (93.3) 

Data are n (%) except for age at end of FY, which is mean ± SD, and last A1C result, which is median (interquartile range).

To develop the prediction model, possible predictors of DKA were first considered based on literature review (68) and the varied clinical expertise of a multidisciplinary team, comprising endocrinologists, advanced practice providers, certified diabetes care and education specialists, psychologists, social workers, data architects, and data analysts. An additional criterion was that predictors had to be able to be extracted automatically from the EMR. Seven variables were eventually selected for testing: patient age at the end of the fiscal year, sex, race/ethnicity, health insurance (public vs. private), most recent A1C value, occurrence of DKA in the prior two fiscal years, and insulin regimen (fixed mealtime dose vs. intensive insulin management [IIM] using an insulin-to-carbohydrate ratio and correction factor vs. insulin pump therapy). Outcome variables were obtained retrospectively from a late-binding enterprise data warehouse (EDW) developed for our institution that draws in data from the EMR and other institutional sources (e.g., financial claims) and a pediatric diabetes analytics application that is able to pull near real-time data from the EDW. The late-binding EDW architecture means that data are not linked to decision rules (such as a risk level cutoff for DKA in our model) until needed, which allows the database to change in response to potential changes in the model (e.g., if new risk factors are identified).

A1C values were obtained as part of routine care using the DCA 2000+ Analyzer point-of-care assessment (Siemens Healthcare Diagnostics, Deerfield, IL). We used the most recent A1C value (rather than an average) so that the index could update automatically whenever a new value was entered into the EMR. This also allows the index to be sensitive to recent changes in A1C, which may place a patient at increased risk. DKA was defined based on ICD-9-CM/ICD-10-CM diagnosis code in the hospital encounter (250.1x, 775.1, 277.09, 790.29), glucose ≥250 mg/dL (≥13 mmol/L), and pH level ≤7.30 and/or administration of intravenous insulin. DKA values were obtained from the preceding 2 years, as looking at only 1 year provided too few data points for analysis. DKA that occurred at diabetes diagnosis was excluded from analysis, as we reasoned that the causes of DKA would differ in newly diagnosed versus established patients. Other potentially important variables (e.g., family conflict and patient mental health) could not be included because of limitations of the data contained in a searchable format in our EMR. We considered using mental health diagnoses, but they are generally only entered into the EMR if a patient is among the subset being seen by the psychiatry or psychology services at our institution, so it was determined that these data would not be an accurate reflection of mental health conditions in our cohort.

Data from FY2016 were divided into training and validation datasets at a 70/30 split. Complete data from the next fiscal year (FY2017) provided the observation period for future DKA events and, as such, was used as an independent test dataset in a series of univariate logistic regression models. For nondichotomous variables, changes in odds were associated with each single-unit increase in the variable (e.g., an interval of 1 year for age and 1% for A1C). To guard against false positives, a Bonferroni-corrected significance level of 0.00625 was used to determine which variables from the univariate logistic regression model moved to the multivariable model. All analyses were conducted using IBM SPSS, v. 26, statistics software.

Four variables emerged as significantly associated with DKA in univariate analyses and were moved forward: A1C, race/ethnicity, type of insurance, and DKA in the past two fiscal years (Table 2). In fitting the multivariable model to the validation dataset, race/ethnicity and health insurance masked each other’s effect, so two reduced models with each of these variables were considered. The model with insurance type performed slightly better than the one with race/ethnicity and thus was moved forward. The final model comprised three variables: most recent A1C (odds ratio [OR] 1.29, 95% CI 1.16–1.45, P <0.0001), type of health insurance (OR 2.27, 95% CI 1.38–3.74, P = 0.0001), and prior DKA (OR 3.10, 95% CI 1.81–5.23, P <0.0001) (Table 3). As can be seen in Figure 1, the model correctly classified 77.6% of patients with regard to presence/absence of a subsequent DKA in FY2017.

FIGURE 1

Receiver operating characteristic (ROC) curve for multivariate logistic model for validation dataset. AUC, area under the curve.

FIGURE 1

Receiver operating characteristic (ROC) curve for multivariate logistic model for validation dataset. AUC, area under the curve.

Close modal
TABLE 2

Univariate Logistic Regression Models on Training Dataset (n = 1,304)

VariableComparisonOR (95% CI)P
Age at the end of FY16 — 0.97 (0.92–1.03) 0.333 
Last A1C result — 1.40 (1.26–1.56) <0.0001 
Sex Female vs. male 1.60 (1.01–2.55) 0.047 
Ethnicity/race Black vs. White NH/other 3.89 (2.15–7.04) <0.0001 
  Hispanic vs. White NH/other 3.68 (2.12–6.40) <0.0001 
Insurance Public/self-pay vs. private/other 3.29 (2.05–5.28) <0.0001 
Insulin regimen Fixed/other vs. pump or IIM 1.96 (1.20–3.22) 0.007 
DKA in prior two FYs Yes vs. no 4.96 (3.00–8.19) <0.0001 
VariableComparisonOR (95% CI)P
Age at the end of FY16 — 0.97 (0.92–1.03) 0.333 
Last A1C result — 1.40 (1.26–1.56) <0.0001 
Sex Female vs. male 1.60 (1.01–2.55) 0.047 
Ethnicity/race Black vs. White NH/other 3.89 (2.15–7.04) <0.0001 
  Hispanic vs. White NH/other 3.68 (2.12–6.40) <0.0001 
Insurance Public/self-pay vs. private/other 3.29 (2.05–5.28) <0.0001 
Insulin regimen Fixed/other vs. pump or IIM 1.96 (1.20–3.22) 0.007 
DKA in prior two FYs Yes vs. no 4.96 (3.00–8.19) <0.0001 

NH, non-Hispanic. For nondichotomous variables, changes in odds were associated with each single-unit increase in the variable (1-year intervals for age, 1% intervals for A1C).

TABLE 3

Multivariable Logistic Regression of DKA in Training Dataset

VariableComparisonAdjusted OR (95% CI)P
Last A1C result* — 1.29 (1.16–1.45) <0.0001 
Insurance Public/self-pay vs. private/other 2.27 (1.38–3.74) 0.001 
DKA in prior two FYs Yes vs. no 3.10 (1.81–5.23) <0.0001 
VariableComparisonAdjusted OR (95% CI)P
Last A1C result* — 1.29 (1.16–1.45) <0.0001 
Insurance Public/self-pay vs. private/other 2.27 (1.38–3.74) 0.001 
DKA in prior two FYs Yes vs. no 3.10 (1.81–5.23) <0.0001 
*

For A1C, change in odds was associated with each 1% increase in the variable.

This three-factor model was then used to create several point-based risk categories. We decided on a three-tier risk stratification system reflecting low-, moderate-, and high-risk categories, which maps onto widely used public health frameworks in pediatrics (16) and is similar to what was used for the RI-PGC. For A1C, a nonlinear point system was created, and the variable was rescaled and centered to zero to give credit to those with low A1C values. The point system was determined based on clinical judgment and allocated as follows: insurance: public/self-pay = 4, private = 0; prior DKA (last 2 fiscal years): yes = 5, no = 0; A1C: <8% = −2, 8.0–8.9% = 0, 9% = 0.5, >9% = +0.05, for every additional half percent up to 14%. The final risk score ranged from −2 to 10, and a three-tier system was implemented such that scores <2 were considered low risk, those 2–6 were moderate risk, and those >6 were high risk. The risk categories were also determined based on clinical judgment as follows: low: no risk factors (−2 to 0 points) or high A1C alone (+0.5 to 1 point); moderate: public insurance (+4 points) with or without a high A1C (+0.5 to 1 point) or prior DKA (+5 points) with private insurance and an A1C ≤8.5% (0 to −2 points); and high: prior DKA (+5 points) plus one other risk factor. As can be seen in Table 4, the risk categorization accurately identified patients at greatest risk for a subsequent DKA episode in the coming year. Most patients fell into the low-risk group, while a little more than one-third fell into the moderate-risk group, and ∼5–7% were identified as having high risk.

TABLE 4

RI-DKA Risk Categories and DKA Incidence by Risk Level

Risk LevelScoren (%)DKA = 0, nDKA = 1, nPercentage With DKA in Risk Level
Training dataset (FY2016, 70% of data) 
Low <2 757 (58.10) 735 22 2.90 
Moderate 2–5.99 459 (35.20) 422 37 8.10 
High ≥6 88 (6.70) 67 21 23.90 
Total — 1,304 (100) 1,224 80 — 
Sensitivity = 0.26, specificity = 0.95, PPV = 0.24, NPV = 0.95, LR+ = 4.80 
Validation dataset (FY2016, 30% of data) 
Low <2 315 (56.30) 309 1.90 
Moderate 2–5.99 216 (38.60) 196 20 9.30 
High ≥6 29 (5.20) 20 31.00 
Total — 560 (100) 525 35 — 
Sensitivity = 0.26, specificity = 0.96, PPV = 0.31, NPV = 0.95, LR+ = 6.75 
Test dataset (FY2017) 
Low <2 1,079 (56.70) 1,051 28 2.60 
Moderate 2–5.99 685 (36.00) 619 66 9.60 
High ≥6 139 (7.30) 106 33 23.70 
Total — 1,903 (100) 1,776 127 — 
Sensitivity = 0.26, specificity = 0.94, PPV = 0.24, NPV = 0.95, LR+ = 4.35 
Risk LevelScoren (%)DKA = 0, nDKA = 1, nPercentage With DKA in Risk Level
Training dataset (FY2016, 70% of data) 
Low <2 757 (58.10) 735 22 2.90 
Moderate 2–5.99 459 (35.20) 422 37 8.10 
High ≥6 88 (6.70) 67 21 23.90 
Total — 1,304 (100) 1,224 80 — 
Sensitivity = 0.26, specificity = 0.95, PPV = 0.24, NPV = 0.95, LR+ = 4.80 
Validation dataset (FY2016, 30% of data) 
Low <2 315 (56.30) 309 1.90 
Moderate 2–5.99 216 (38.60) 196 20 9.30 
High ≥6 29 (5.20) 20 31.00 
Total — 560 (100) 525 35 — 
Sensitivity = 0.26, specificity = 0.96, PPV = 0.31, NPV = 0.95, LR+ = 6.75 
Test dataset (FY2017) 
Low <2 1,079 (56.70) 1,051 28 2.60 
Moderate 2–5.99 685 (36.00) 619 66 9.60 
High ≥6 139 (7.30) 106 33 23.70 
Total — 1,903 (100) 1,776 127 — 
Sensitivity = 0.26, specificity = 0.94, PPV = 0.24, NPV = 0.95, LR+ = 4.35 

LR+, positive likelihood ratio; NPV, negative predictive value; PPV, positive predictive value.

This article reports on an innovative, automated, EMR-based algorithm for predicting risk for DKA in established pediatric patients with type 1 diabetes. Scores are updated every time new relevant data such as an updated A1C result, a new episode of DKA, or a change in insurance are added to the EMR. This strategy allows the tool to provide continually updated risk assessments that can be used by providers to guide clinical care in real time. The results are logged in the EMR, and a chart can be brought up on screen quickly showing the current risk level, as well as 3-month and 1-, 2-, and 5-year trends. A next step in implementation will be to create a best practice alert that automatically identifies at-risk patients whenever their provider opens their chart.

Our findings converge closely with the results of another recent study examining DKA risk in a cohort of 1,428 patients with type 1 diabetes (17) that also found that a three-factor model including prior hospitalizations (for DKA, hyperglycemia, and ketonemia), A1C, and insurance status was the best predictor of DKA. That a similar model emerged from our data provides strong converging evidence for the importance of these variables in predicting DKA. Risk for DKA has also been found to be associated with insurance status in large-scale studies using the U.S. National Readmission Database (18) and the T1D Exchange clinic registry (19), and with higher A1C in multiple studies (8,19,20). Female sex has also frequently been found to be associated with DKA (6,1921), so it is unclear why this was not a significant predictor in our study. It is possible that sex is not a significant factor by itself but only in interaction with other variables. Post-hoc analyses revealed potentially interesting interactions between sex and race/ethnicity in our cohort, but we did not have sufficient power to fully test these relationships. In addition, Rewers et al. (20) found that psychiatric disorders had a significantly larger effect on DKA risk in girls compared with boys. These potential interactions deserve further study in larger cohorts of patients.

A primary goal of risk identification and stratification is to guide allocation of scarce preventive intervention resources to the patients most in need of them. To this end, we are in the process of designing preventive interventions graded to patients’ level of risk. Universal diabetes education focused on DKA prevention has been shown to be effective in reducing DKA admission rates (22). Use of diabetes technologies such as insulin pump therapy and real-time continuous glucose monitoring (CGM) also has the potential to reduce the incidence of DKA in the diabetes population (23). A promising intervention targeted to the highest-risk patients is the Novel Interventions in Children’s Healthcare (NICH) program, which provides intensive services, including coordination of care, help with finding community resources, and training in problem-solving skills. Initial data suggest that NICH may be effective in reducing the incidence of DKA and health care costs in high-risk youth with type 1 diabetes (24).

Understanding the degree of risk is a first step toward risk reduction, but for intervention purposes, it is also important to understand the type of risk, and this is not currently well captured in our index. Youth may be more prone to DKA for very different reasons (e.g., because they omit insulin to control weight gain, because they do not understand appropriate sick-day management, because of mental health issues such as depression, or because their insurance has lapsed leaving them unable to fill their last prescription). More comprehensive psychosocial assessment of high-risk patients is likely to be crucially important for tailoring interventions to each patient’s needs.

One limitation to the risk index as currently designed is that it is unlikely to be accurate if patients do not have a recent A1C value in their chart (e.g., if they do not come to the clinic or go to a laboratory to get their A1C test). These patients may not have been captured in our cohort, which was limited to patients who had at least one clinic visit in each fiscal year analyzed. Missed clinic visits have been especially acute during the coronavirus disease 2019 pandemic, although more generally there is a known association between patients at highest risk for problematic diabetes-related outcomes and poor clinic attendance (25). Thus, there may be a cohort of high-risk patients who lack an updated risk score using our EMR-based tool. One option might be to use an estimated A1C based on mean blood glucose (26) or glucose management indicator (27), but these options would only be feasible for patients frequently checking blood glucose with a glucose meter or using CGM. Another option might be to use telehealth services to reduce the overall number of in-person visits required (e.g., by having patients go to an outside laboratory for their A1C test or use an at-home A1C test kit). There is evidence that at-home kits can accurately measure A1C levels (28), although there are logistic challenges to their use (e.g., mailing the kits to patients and having them returned), and they are frequently not covered by insurance. Still, some recent emerging evidence suggests that telehealth visits may help prevent DKA in higher-risk patients (29).

This study has a number of other limitations. First, the risk index was developed based on data from a single site. Although we were able to use a relatively large dataset drawing from a diverse patient population, it remains possible that different predictors would have emerged as significant in a different dataset drawing from a different sample. In addition, the test dataset was largely drawn from the same cohort as the training dataset, potentially inflating model performance. Our next step will be to externally validate the RI-DKA at other institutions and/or through large-scale diabetes databases. Another limitation is that the RI-DKA does not draw in psychological and mental health data (e.g., symptoms of depression), which have been shown to be an important additional contributor to DKA risk (21,30). To address this shortcoming, we plan to integrate data from the RI-PGC, the RI-DKA, and a screener for symptoms of depression that we have been using at our site (the Patient Health Questionnaire-9 [31]) to see whether predictive validity of the risk index can be further improved.

In conclusion, we have shown that risk for DKA can be estimated by an automated algorithm that draws from data easily available in the EMR and provides real-time support for clinical decision-making. More specifically, the risk index identifies patients with a significantly increased likelihood of experiencing an episode of DKA in the next year. Targeting these patients for preventive interventions has the potential to reduce the serious human and financial costs associated with this dangerous condition (9).

Duality of Interest

No potential conflicts of interest relevant to this article were reported.

Author Contributions

D.D.S. helped to create the risk score, wrote the manuscript, researched data, and reviewed/edited the manuscript. R.B. researched data, helped to create the risk score, completed the data analysis, wrote the Research Design and Methods and results sections, and reviewed/edited the manuscript. S.U. researched data, wrote the introduction, and reviewed/edited the manuscript. M.V. researched data, contributed to writing the manuscript, and reviewed/edited the manuscript. K.R.H. and K.F.-B. led the high-risk team and efforts to create the risk score and reviewed/edited the manuscript. S.K.L., R.S., and S.K.G. reviewed/edited the manuscript. S.D.-T. also led the high-risk team, researched data, contributed to the Research Design and Methods section, and reviewed/edited the manuscript. D.D.S. is the guarantor of this work and, as such, had full access to all the data and takes responsibility for the integrity of the data and the accuracy of the data analyses.

1.
Bialo
SR
,
Agrawal
S
,
Boney
CM
,
Quintos
JB
.
Rare complications of pediatric diabetic ketoacidosis
.
World J Diabetes
2015
;
6
:
167
174
2.
Cengiz
E
,
Xing
D
,
Wong
JC
, et al.;
T1D Exchange Clinic Network
.
Severe hypoglycemia and diabetic ketoacidosis among youth with type 1 diabetes in the T1D Exchange clinic registry
.
Pediatr Diabetes
2013
;
14
:
447
454
3.
Desai
D
,
Mehta
D
,
Mathias
P
,
Menon
G
,
Schubart
UK
.
Health care utilization and burden of diabetic ketoacidosis in the U.S. over the past decade: a nationwide analysis
.
Diabetes Care
2018
;
41
:
1631
1638
4.
Wolfsdorf
J
,
Craig
ME
,
Daneman
D
, et al
.
Diabetic ketoacidosis in children and adolescents with diabetes
.
Pediatr Diabetes
2009
;
10
(
Suppl. 12
):
118
133
5.
Bismuth
E
,
Laffel
L
.
Can we prevent diabetic ketoacidosis in children?
Pediatr Diabetes
2007
;
8
(
Suppl. 6
):
24
33
6.
Fritsch
M
,
Rosenbauer
J
,
Schober
E
,
Neu
A
,
Placzek
K
;
German Competence Network Diabetes Mellitus and the DPV Initiative
.
Predictors of diabetic ketoacidosis in children and adolescents with type 1 diabetes: experience from a large multicentre database
.
Pediatr Diabetes
2011
;
12
:
307
312
7.
Maniatis
AK
,
Goehrig
SH
,
Gao
D
,
Rewers
A
,
Walravens
P
,
Klingensmith
GJ
.
Increased incidence and severity of diabetic ketoacidosis among uninsured children with newly diagnosed type 1 diabetes mellitus
.
Pediatr Diabetes
2005
;
6
:
79
83
8.
Semenkovich
K
,
Berlin
KS
,
Ankney
RL
, et al
.
Predictors of diabetic ketoacidosis hospitalizations and hemoglobin A1c among youth with type 1 diabetes
.
Health Psychol
2019
;
38
:
577
585
9.
Vellanki
P
,
Umpierrez
GE
.
Increasing hospitalizations for DKA: a need for prevention programs
.
Diabetes Care
2018
;
41
:
1839
1841
10.
Zhong
VW
,
Juhaeri
J
,
Mayer-Davis
EJ
.
Trends in hospital admission for diabetic ketoacidosis in adults with type 1 and type 2 diabetes in England, 1998–2013: a retrospective cohort study
.
Diabetes Care
2018
;
41
:
1870
1877
11.
Harris
MA
,
Spiro
K
,
Heywood
M
, et al
.
Novel Interventions in Children’s Health Care (NICH): innovative treatment for youth with complex medical conditions
.
Clin Pract Pediatr Psychol
2013
;
1
:
137
145
12.
Hippisley-Cox
J
,
Coupland
C
,
Robson
J
,
Sheikh
A
,
Brindle
P
.
Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore
.
BMJ
2009
;
338
:
b880
13.
D’Agostino
RB
Sr
,
Vasan
RS
,
Pencina
MJ
, et al
.
General cardiovascular risk profile for use in primary care: the Framingham Heart Study
.
Circulation
2008
;
117
:
743
753
14.
Schwartz
DD
,
Axelrad
ME
,
Anderson
BJ
.
A psychosocial risk index for poor glycemic control in children and adolescents with type 1 diabetes
.
Pediatr Diabetes
2014
;
15
:
190
197
15.
Schwartz
DD
,
Axelrad
MA
,
Anderson
BJ
.
Psychosocial risk screening of children newly diagnosed with type 1 diabetes: a training toolkit for healthcare professionals
.
MedEdPORTAL
.
Published online 23 December 2013 (doi:10.15766/mep_2374- 8265.9643)
16.
Kazak
AE
.
Pediatric Psychosocial Preventative Health Model (PPPHM): research, practice and collaboration in pediatric family systems medicine
.
Fam Syst Health
2006
;
24
:
381
395
17.
Mejia-Otero
JD
,
Adhikari
S
,
White
PC
.
Risk factors for hospitalization in youth with type 1 diabetes: development and validation of a multivariable prediction model
.
Pediatr Diabetes
2020
;
21
:
1268
1276
18.
Everett
E
,
Mathioudakis
NN
.
Association of socioeconomic status and DKA readmission in adults with type 1 diabetes: analysis of the US National Readmission Database
.
BMJ Open Diabetes Res Care
2019
;
7
:
e000621
19.
Maahs
DM
,
Hermann
JM
,
Holman
N
, et al.;
National Paediatric Diabetes Audit and the Royal College of Paediatrics and Child Health, the DPV Initiative, and the T1D Exchange Clinic Network
.
Rates of diabetic ketoacidosis: international comparison with 49,859 pediatric patients with type 1 diabetes from England, Wales, the U.S., Austria, and Germany
.
Diabetes Care
2015
;
38
:
1876
1882
20.
Rewers
A
,
Chase
HP
,
Mackenzie
T
, et al
.
Predictors of acute complications in children with type 1 diabetes
.
JAMA
2002
;
287
:
2511
2518
21.
Bhatt
P
,
Dave
M
,
Amponsah
JK
, et al
.
Etiologies, trends, and predictors of 30-day pediatric readmissions after hospitalizations for diabetic ketoacidosis in the United States
.
Pediatr Diabetes
2020
;
21
:
969
978
22.
Ilkowitz
JT
,
Choi
S
,
Rinke
ML
,
Vandervoot
K
,
Heptulla
RA
.
Pediatric type 1 diabetes: reducing admission rates for diabetes ketoacidosis
.
Qual Manag Health Care
2016
;
25
:
231
237
23.
Wong
JC
,
Foster
NC
,
Maahs
DM
, et al.;
T1D Exchange Clinic Network
.
Real-time continuous glucose monitoring among participants in the T1D Exchange clinic registry
.
Diabetes Care
2014
;
37
:
2702
2709
24.
Wagner
DV
,
Chuong
R
,
Koskela
NC
, et al
.
Ketones to success: does NICH involvement influence DKA discharge presentation? [Abstract]
Diabetes
2018
;
67
(
Suppl. 1
):
805-P
25.
Markowitz
JT
,
Volkening
LK
,
Laffel
LM
.
Care utilization in a pediatric diabetes clinic: cancellations, parental attendance, and mental health appointments
.
J Pediatr
2014
;
164
:
1384
1389
26.
Nathan
DM
,
Kuenen
J
,
Borg
R
,
Zheng
H
,
Schoenfeld
D
;
A1c-Derived Average Glucose Study Group
.
Translating the A1C assay into estimated average glucose values
.
Diabetes Care
2008
;
31
:
1473
1478
27.
Bergenstal
RM
,
Beck
RW
,
Close
KL
, et al
.
Glucose management indicator (GMI): a new term for estimating A1C from continuous glucose monitoring
.
Diabetes Care
2018
;
41
:
2275
2280
28.
Chang
A
,
Frank
J
,
Knaebel
J
,
Fullam
J
,
Pardo
S
,
Simmons
DA
.
Evaluation of an over-the-counter glycated hemoglobin (A1C) test kit
.
J Diabetes Sci Technol
2010
;
4
:
1495
1503
29.
Peters
AL
,
Garg
SK
.
The silver lining to COVID-19: avoiding diabetic ketoacidosis admissions with telehealth
.
Diabetes Technol Ther
2020
;
22
:
449
453
30.
Wagner
DV
,
Stoeckel
M
,
Tudor
ME
,
Harris
MA
.
Treating the most vulnerable and costly in diabetes
.
Curr Diab Rep
2015
;
15
:
606
31.
Richardson
LP
,
McCauley
E
,
Grossman
DC
, et al
.
Evaluation of the Patient Health Questionnaire-9 Item for detecting major depression among adolescents
.
Pediatrics
2010
;
126
:
1117
1123
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license.