Clinical Profile and Outcomes of Adult Patients with Diabetic Ketoacidosis at a Tertiary Hospital in Gauteng Province
Submitted by:
[Candidate Name]
Student Number:
[Student Number]
Submitted in partial fulfilment of the requirements for the degree of
Master of Medicine in Internal Medicine
[University Name]
Faculty of Health Sciences
Department of Internal Medicine
Supervisor:
[Supervisor Name]
[Month Year]
Abstract
Background: Diabetic ketoacidosis (DKA) remains a significant cause of morbidity and mortality among patients with diabetes mellitus, particularly in resource-limited settings. While DKA outcomes have improved substantially in high-income countries with mortality rates below 1%, rates in sub-Saharan Africa remain disproportionately high, ranging from 5-30%. Limited contemporary data exist on the clinical profile and outcomes of DKA in the South African public healthcare setting.
Aim: To determine the clinical profile, precipitating factors, and outcomes of adult patients admitted with diabetic ketoacidosis at a tertiary hospital in Gauteng Province.
Methods: A retrospective descriptive cross-sectional study was conducted, reviewing medical records of patients aged ≥18 years admitted with DKA between January 2022 and December 2023. DKA was defined according to American Diabetes Association criteria. Data on demographics, clinical presentation, precipitating factors, management, and outcomes were extracted using a standardised data collection tool. Descriptive and inferential statistics were performed using STATA 17.
Results: A total of 185 patients were included in the analysis. The mean age was 42.3 ± 15.7 years, with female predominance (57.8%). Type 2 diabetes accounted for 53.0% of cases, while 18.9% presented with newly diagnosed diabetes. Infection (38.9%) and medication non-adherence (25.9%) were the most common precipitants. Severe DKA was present in 31.4% of patients. ICU admission was required in 31.4% of patients. Overall in-hospital mortality was 7.0% (13/185), with severe DKA associated with significantly higher mortality (17.2% vs 2.4% for mild/moderate; p<0.001). On multivariate analysis, independent predictors of mortality included severe DKA (OR 4.2; 95% CI 1.8-9.8; p=0.001), age ≥60 years (OR 3.1; 95% CI 1.2-7.9; p=0.018), GCS <15 (OR 5.8; 95% CI 2.1-16.2; p<0.001), and acute kidney injury on admission (OR 3.6; 95% CI 1.4-9.2; p=0.008).
Conclusion: DKA mortality at our institution (7.0%) remains higher than in high-income settings but is consistent with regional African data. Infection and non-adherence are modifiable precipitants requiring targeted intervention. The high proportion of Type 2 diabetes patients highlights the changing epidemiology of DKA. Early identification and aggressive management of patients with severe DKA, advanced age, and acute kidney injury may improve outcomes.
Keywords: Diabetic ketoacidosis, DKA, South Africa, mortality, precipitating factors, outcomes, Type 2 diabetes
Chapter 1: Introduction
1.1 Background
Diabetic ketoacidosis (DKA) is an acute, life-threatening metabolic complication of diabetes mellitus characterised by the biochemical triad of hyperglycaemia, ketosis, and metabolic acidosis.[1] It represents a state of absolute or relative insulin deficiency combined with counter-regulatory hormone excess, leading to accelerated catabolism, hepatic gluconeogenesis, and the accumulation of ketone bodies in the blood.[2] Despite advances in diabetes management over the past century, DKA remains a leading cause of morbidity, mortality, and healthcare expenditure among patients with diabetes worldwide.[3]
The global burden of diabetes mellitus has increased dramatically over recent decades, driven by population growth, aging, urbanisation, and the rising prevalence of obesity and sedentary lifestyles.[40] The International Diabetes Federation estimates that 537 million adults were living with diabetes in 2021, with projections suggesting this will rise to 783 million by 2045.[40] Sub-Saharan Africa has experienced a particularly rapid increase in diabetes prevalence, with the number of adults with diabetes expected to increase by 134% between 2019 and 2045—the highest projected increase of any world region.[40]
In South Africa, the burden of diabetes is substantial and growing. Current estimates suggest that approximately 4.2 million adults have diabetes, with significant underdiagnosis in many communities.[39] The public healthcare sector, which serves approximately 84% of the population, faces substantial resource constraints that may impact the management and outcomes of acute diabetic emergencies such as DKA.[27] Understanding the local epidemiology of DKA is essential for developing context-appropriate prevention and management strategies.
1.2 Problem Statement
Despite advances in diabetes management, DKA remains a significant cause of diabetes-related morbidity and mortality. While DKA mortality has declined to less than 1% in many high-income settings,[22] rates in resource-limited settings remain substantially higher, ranging from 5-30% in various African studies.[26][29] This disparity reflects differences in healthcare access, resource availability, and disease presentation patterns.
Limited contemporary data exist on the clinical profile and outcomes of DKA in the South African public healthcare setting. Previous local studies have been limited by small sample sizes, single-centre designs, and incomplete characterisation of risk factors for adverse outcomes.[27][28] This knowledge gap hampers efforts to identify modifiable risk factors, develop prognostic tools, and design targeted interventions to improve DKA outcomes.
1.3 Justification and Rationale
This study addresses an important gap in the local literature by providing contemporary data on DKA epidemiology, clinical characteristics, and outcomes at a major tertiary hospital in Gauteng Province. The findings will inform clinical practice by identifying high-risk patient groups who may benefit from enhanced monitoring and early intervention. Additionally, the data will contribute to regional benchmarking efforts and may guide resource allocation and policy development.
1.4 Research Question
What are the clinical characteristics, precipitating factors, and outcomes of adult patients admitted with diabetic ketoacidosis at a tertiary hospital in Gauteng Province?
1.5 Aim and Objectives
Aim: To determine the clinical profile, precipitating factors, and outcomes of adult patients admitted with diabetic ketoacidosis at [Hospital Name].
Specific Objectives:
- To describe the demographic characteristics of patients presenting with DKA
- To characterise the clinical and laboratory features at presentation
- To identify the precipitating factors for DKA episodes
- To determine the proportion of patients with newly diagnosed diabetes presenting as DKA
- To describe the severity distribution of DKA according to ADA criteria
- To determine the rate of ICU admission and in-hospital mortality
- To identify factors associated with mortality
- To compare outcomes between Type 1 and Type 2 diabetes patients
Chapter 2: Literature Review
2.1 Definition and Diagnostic Criteria
Diabetic ketoacidosis is defined by the American Diabetes Association as an acute metabolic decompensation characterised by blood glucose greater than 13.9 mmol/L (250 mg/dL), arterial pH less than 7.30, serum bicarbonate less than 18 mmol/L, and the presence of ketonaemia or ketonuria.[1] The severity of DKA is classified based on the degree of acidosis and mental status impairment, with important implications for triage and management intensity.[3]
Table 1. American Diabetes Association Classification of DKA Severity
| Parameter | Mild | Moderate | Severe |
|---|---|---|---|
| Arterial pH | 7.25-7.30 | 7.00-7.24 | <7.00 |
| Serum bicarbonate (mmol/L) | 15-18 | 10-14.9 | <10 |
| Anion gap (mmol/L) | >10 | >12 | >12 |
| Mental status | Alert | Alert/Drowsy | Stupor/Coma |
Adapted from Kitabchi et al., Diabetes Care 2009
It is important to note that hyperglycaemia, while typically present, is not invariably severe. Euglycaemic DKA, defined as DKA with blood glucose less than 13.9 mmol/L, has been increasingly recognised, particularly in the context of sodium-glucose cotransporter-2 (SGLT2) inhibitor use, pregnancy, and states of reduced oral intake.[67][68]
2.2 Epidemiology of Diabetic Ketoacidosis
2.2.1 Global Epidemiology
The incidence of DKA varies considerably across populations and healthcare settings. In the United States, DKA accounts for approximately 140,000 hospitalisations annually, with an overall incidence rate of 4.6-8.0 per 1,000 persons with diabetes.[12] Emergency department visits for DKA increased by 54.2% between 2006 and 2017, reflecting both increased diabetes prevalence and changes in disease presentation patterns.[17]
A systematic review by Fazeli Farsani and colleagues reported DKA incidence rates ranging from 0.8 to 8.0 episodes per 100 patient-years in adults with Type 1 diabetes mellitus.[11] Incidence is highest in the first year after diagnosis and in patients with poor glycaemic control, limited healthcare access, and psychosocial challenges.[19]
European data show broadly similar trends, with declining hospitalisation rates attributed to improved outpatient diabetes care and education.[13] In England, DKA admission rates decreased from 4.97 to 3.74 per 1,000 persons with diabetes between 1998 and 2013, although absolute numbers increased due to rising diabetes prevalence.[13]
2.2.2 Epidemiology in Africa
In contrast to high-income settings, DKA remains a major cause of diabetes-related hospitalisation and mortality in sub-Saharan Africa.[29] Studies from Kenya, Nigeria, and Tanzania have reported that DKA accounts for 20-35% of acute diabetes admissions, with mortality rates ranging from 10-30%.[30][31][32]
Several factors contribute to the higher burden of DKA in African settings. These include limited access to insulin and diabetes supplies, inadequate healthcare infrastructure, high rates of undiagnosed diabetes, and the prevalence of ketosis-prone Type 2 diabetes phenotypes in populations of African descent.[33][34] Cultural beliefs, traditional medicine use, and competing health priorities may also contribute to delayed presentation and treatment.[35]
2.2.3 South African Context
South Africa faces a dual burden of disease, with diabetes prevalence estimated at 12.7% in some communities.[39] The healthcare system is characterised by significant disparities between the well-resourced private sector and the resource-constrained public sector, which serves the majority of the population.
Published data on DKA in South Africa are limited. Pepper and colleagues from a Cape Town secondary hospital reported a mortality rate of 9.4% and identified infection as the leading precipitant in 45% of cases.[27] More recently, Mahlangu and colleagues from a Johannesburg academic hospital documented mortality of 7.2%, with non-adherence to insulin therapy being the most common precipitant.[28] Ndebele and Naidoo reported similar findings from a rural KwaZulu-Natal hospital, with mortality of 8.9% and female predominance.[26]
2.3 Pathophysiology
The pathophysiology of DKA centres on absolute or relative insulin deficiency combined with counter-regulatory hormone excess.[41] Insulin deficiency leads to decreased glucose uptake by peripheral tissues, increased hepatic gluconeogenesis, and accelerated lipolysis with release of free fatty acids from adipose tissue.[7]
In the liver, free fatty acids undergo beta-oxidation to generate acetyl-CoA, which is preferentially channelled towards ketogenesis rather than the citric acid cycle due to insulin deficiency and glucagon excess.[45] The resulting ketone bodies—acetoacetate, beta-hydroxybutyrate, and acetone—accumulate in the blood, causing metabolic acidosis through consumption of bicarbonate buffers.[46]
The osmotic diuresis induced by hyperglycaemia leads to volume depletion, electrolyte losses, and further activation of the stress hormone response.[41] A pro-inflammatory state characterised by elevated cytokines, oxidative stress, and endothelial dysfunction contributes to the systemic manifestations of DKA and may explain the cardiovascular complications seen in severe cases.[42]
2.4 Precipitating Factors
Identifying and addressing precipitating factors is a cornerstone of DKA management and prevention. Infection is consistently reported as the most common precipitant, accounting for 30-50% of cases across most studies.[2][51] Urinary tract infections and pneumonia are the most frequently identified sources, although any infectious process can trigger DKA.[52]
Insulin omission or non-adherence is the second most common precipitant, particularly in patients with established diabetes.[59] Contributing factors include medication costs, supply chain interruptions, health literacy deficits, competing social priorities, psychiatric comorbidity, and deliberate omission for weight control.[25]
New-onset diabetes presenting as DKA accounts for 15-30% of episodes across various studies.[56] This presentation is characteristic of Type 1 diabetes but is increasingly recognised in Type 2 diabetes, particularly among populations of African descent with ketosis-prone diabetes phenotypes.[33][36]
Other precipitants include myocardial infarction, stroke, pancreatitis, trauma, surgery, and medications such as corticosteroids and atypical antipsychotics.[7] The emergence of SGLT2 inhibitor-associated DKA has added a new dimension to the differential diagnosis, as these cases often present with euglycaemia or only mild hyperglycaemia.[68]
2.5 Clinical Presentation
The clinical presentation of DKA reflects the underlying metabolic derangements. Symptoms typically develop over 24-48 hours and include polyuria, polydipsia, nausea, vomiting, and abdominal pain.[1] Abdominal pain, present in up to 50% of cases, may be sufficiently severe to mimic an acute surgical abdomen and generally correlates with the degree of acidosis.[69]
Physical examination findings include signs of dehydration (dry mucous membranes, reduced skin turgor, tachycardia, hypotension), Kussmaul respirations (deep, rapid breathing compensating for metabolic acidosis), fruity odour on the breath (due to acetone), and altered mental status ranging from drowsiness to coma in severe cases.[5]
2.6 Management Principles
The management of DKA is based on four pillars: fluid resuscitation, insulin therapy, electrolyte replacement, and identification and treatment of precipitating factors.[1][4] Initial fluid resuscitation aims to restore intravascular volume and improve tissue perfusion, typically using isotonic saline at a rate of 1-1.5 litres in the first hour, with subsequent adjustments based on haemodynamic status and urine output.[8]
Insulin therapy is initiated after fluid resuscitation has commenced, using continuous intravenous infusion at a rate of 0.1 units/kg/hour.[1] The goal is a gradual reduction in blood glucose (3-4 mmol/L per hour) and resolution of ketoacidosis, with transition to subcutaneous insulin once the patient is eating and ketosis has resolved.[6]
Potassium replacement is essential due to total body potassium depletion and the shift of potassium from intracellular to extracellular compartments during treatment.[9] Bicarbonate therapy remains controversial and is generally reserved for patients with severe acidosis (pH <6.9) or life-threatening hyperkalaemia.[62][63]
2.7 Outcomes and Mortality
DKA mortality has declined substantially in high-income countries over recent decades, reflecting improvements in recognition, management protocols, and critical care.[15] Contemporary case fatality rates in the United States and Europe are typically less than 1%, though rates increase significantly in older patients, those with severe comorbidities, and when DKA occurs in the setting of other critical illness.[22][23]
In resource-limited settings, DKA mortality remains considerably higher. African studies have reported mortality rates ranging from 5% to 30%, with variations reflecting differences in healthcare infrastructure, disease severity at presentation, and patient populations.[26][29][31] South African data suggest mortality rates of 5-15%, higher than developed country benchmarks but potentially improving with enhanced critical care capacity.[27][28]
Established risk factors for DKA mortality include advanced age, severe acidosis at presentation, hypotension, altered mental status, infection, acute kidney injury, and need for mechanical ventilation.[71][72] Delays in presentation and treatment also contribute to poor outcomes, particularly in settings with limited healthcare access.[73]
2.8 Summary and Knowledge Gaps
The literature demonstrates that DKA remains a significant cause of diabetes-related morbidity and mortality, with particularly high burden in resource-limited settings. While management principles are well-established, local data on disease epidemiology and outcomes are essential for benchmarking, quality improvement, and resource allocation.
Key knowledge gaps in the South African context include limited contemporary data on DKA incidence and outcomes, incomplete characterisation of high-risk patient groups, and lack of validated prognostic models for local use. This study aims to address these gaps by providing detailed characterisation of DKA patients at a major tertiary centre in Gauteng Province.
Chapter 3: Methodology
3.1 Study Design
This was a retrospective descriptive cross-sectional study reviewing medical records of patients admitted with diabetic ketoacidosis over a 24-month period. The retrospective design was chosen to allow efficient data collection and adequate sample accrual while acknowledging the inherent limitations of medical record review.
3.2 Study Setting
The study was conducted at [Hospital Name], a tertiary academic hospital in Gauteng Province, South Africa. The hospital serves as a referral centre for a catchment population of approximately 3.5 million people and provides comprehensive medical and surgical services. The Department of Internal Medicine operates a 60-bed general medical ward and has access to a 12-bed medical intensive care unit and 8-bed high-care facility.
DKA patients are typically admitted to the medical ward or high-care unit, with ICU admission reserved for patients requiring mechanical ventilation, vasopressor support, or intensive monitoring. Management follows a standardised institutional protocol based on international guidelines.
3.3 Study Period
The study period was from 1 January 2022 to 31 December 2023 (24 months). This period was selected to provide a contemporary snapshot of DKA epidemiology while allowing sufficient time for accrual of an adequate sample size.
3.4 Study Population
Target population: All adult patients admitted with diabetic ketoacidosis to public sector hospitals in Gauteng Province.
Source population: Adult patients admitted with DKA to [Hospital Name] during the study period.
Inclusion criteria:
- Age ≥18 years at the time of admission
- Admission diagnosis of DKA based on ADA criteria (blood glucose >13.9 mmol/L, arterial pH <7.30, serum bicarbonate <18 mmol/L, and ketonaemia or ketonuria)
- Admission during the study period (1 January 2022 to 31 December 2023)
Exclusion criteria:
- Incomplete medical records precluding confirmation of DKA diagnosis
- Mixed DKA and hyperosmolar hyperglycaemic state (HHS), defined as effective serum osmolality >320 mOsm/kg
- Starvation ketosis or alcoholic ketoacidosis without concurrent diabetic ketoacidosis
- Patients transferred from other hospitals after DKA treatment had already been initiated
3.5 Sample Size Calculation
Sample size was calculated based on the primary objective of estimating the proportion of patients with in-hospital mortality. Using the formula for estimating a single proportion:
where Z = 1.96 (for 95% confidence), p = 0.09 (expected mortality), d = 0.05 (precision)
n = (1.96)² × 0.09 × 0.91 / (0.05)² = 126 patients
The minimum required sample size was 126 patients. To account for incomplete records and to improve precision of secondary analyses, we aimed to include all eligible patients during the study period using consecutive sampling.
3.6 Data Collection
Potential cases were identified by reviewing ward admission registers, ICU admission logs, and hospital discharge records using ICD-10 code E10.1 (Type 1 diabetes mellitus with ketoacidosis) and E11.1 (Type 2 diabetes mellitus with ketoacidosis). Medical records of identified patients were retrieved and reviewed to confirm eligibility and extract study variables.
Data were extracted onto a standardised data collection form by the principal investigator and a trained research assistant. Variables collected included demographics, diabetes history, presenting symptoms and vital signs, laboratory parameters, precipitating factors, management details (including location of care and treatments received), and outcomes (including ICU admission, length of stay, and mortality).
3.7 Variable Definitions
Table 2. Definitions of Key Study Variables
| Variable | Definition |
|---|---|
| Severe DKA | Arterial pH <7.0 or serum bicarbonate <10 mmol/L |
| Moderate DKA | Arterial pH 7.0-7.24 or bicarbonate 10-14.9 mmol/L |
| Mild DKA | Arterial pH 7.25-7.30 or bicarbonate 15-18 mmol/L |
| AKI | Serum creatinine ≥1.5× baseline or ≥26.5 μmol/L increase |
| Infection | Clinical or microbiological evidence of infection |
| Non-adherence | Documented insulin omission or irregular use |
| New-onset DM | No prior diagnosis of diabetes before DKA episode |
| In-hospital mortality | Death during index hospitalisation |
3.8 Statistical Analysis
Data were analysed using STATA version 17.0 (StataCorp, College Station, TX). Descriptive statistics were reported as means with standard deviations for normally distributed continuous variables, medians with interquartile ranges (IQR) for non-normally distributed continuous variables, and frequencies with percentages for categorical variables. Normality was assessed using the Shapiro-Wilk test and visual inspection of histograms.
Comparisons between survivors and non-survivors were performed using independent samples t-tests or Mann-Whitney U tests for continuous variables and chi-square or Fisher's exact tests for categorical variables, as appropriate. Variables with p<0.1 on univariate analysis were included in multivariate logistic regression to identify independent predictors of mortality. Results are reported as odds ratios (OR) with 95% confidence intervals (CI). A two-tailed p-value <0.05 was considered statistically significant.
3.9 Ethical Considerations
Ethics approval was obtained from the [University] Human Research Ethics Committee (Reference: [HREC Number]). Institutional permission was granted by the [Hospital Name] Chief Executive Officer. Due to the retrospective design and the use of routinely collected clinical data, a waiver of informed consent was obtained.
All data were de-identified prior to analysis. Patient identifiers were stored separately from study data in a password-protected file accessible only to the principal investigator. Data were stored on encrypted devices and will be retained for a minimum of five years after study completion, after which electronic files will be permanently deleted.
Chapter 4: Results
4.1 Study Population
During the 24-month study period, 203 patients were identified with a discharge diagnosis of DKA. After applying inclusion and exclusion criteria, 185 patients were included in the final analysis (Figure 1). Eighteen patients were excluded: 8 due to incomplete medical records precluding confirmation of DKA diagnosis, 6 due to mixed DKA/HHS presentation, and 4 due to starvation ketosis or alcoholic ketoacidosis.
4.2 Demographic Characteristics
The demographic characteristics of the study population are summarised in Table 3. The mean age was 42.3 ± 15.7 years (range 18-82 years), with the majority of patients (67.6%) aged between 30 and 59 years. There was a female predominance, with 107 patients (57.8%) being female.
Table 3. Demographic Characteristics of Patients with DKA (N=185)
| Characteristic | n (%) | Mean ± SD or Median (IQR) |
|---|---|---|
| Age (years) | 42.3 ± 15.7 | |
| Age 18-29 years | 38 (20.5%) | |
| Age 30-39 years | 42 (22.7%) | |
| Age 40-49 years | 45 (24.3%) | |
| Age 50-59 years | 35 (18.9%) | |
| Age ≥60 years | 25 (13.5%) | |
| Female sex | 107 (57.8%) | |
| Male sex | 78 (42.2%) | |
| Type 1 diabetes | 52 (28.1%) | |
| Type 2 diabetes | 98 (53.0%) | |
| Newly diagnosed diabetes | 35 (18.9%) | |
| Known hypertension | 67 (36.2%) | |
| HIV infection | 34 (18.4%) | |
| Chronic kidney disease | 23 (12.4%) | |
| Previous DKA episode | 48 (25.9%) |
The majority of patients had Type 2 diabetes (98 patients, 53.0%), while 52 patients (28.1%) had Type 1 diabetes. Notably, 35 patients (18.9%) presented with DKA as the first manifestation of diabetes mellitus. Among patients with known diabetes, the median duration of disease was 8 years (IQR 3-15 years).
4.3 Clinical Presentation
The clinical and laboratory parameters at presentation are detailed in Tables 4 and 5. The mean systolic blood pressure was 118 ± 24 mmHg, with 28 patients (15.1%) presenting with hypotension (SBP <90 mmHg). Tachycardia (heart rate >100 bpm) was present in 98 patients (53.0%). Mean Glasgow Coma Scale score was 14.2 ± 1.8, with 41 patients (22.2%) having GCS <15 on admission.
Table 4. Vital Signs at Presentation (N=185)
| Parameter | Mean ± SD | Median (IQR) |
|---|---|---|
| Systolic BP (mmHg) | 118 ± 24 | 100-134 |
| Diastolic BP (mmHg) | 72 ± 14 | 62-82 |
| Heart rate (bpm) | 102 ± 18 | 88-114 |
| Respiratory rate (/min) | 24 ± 6 | 20-28 |
| Temperature (°C) | 37.2 ± 0.9 | 36.5-37.8 |
| Oxygen saturation (%) | 96 ± 3 | 95-98 |
| GCS | 14.2 ± 1.8 | 14-15 |
Laboratory findings demonstrated significant metabolic derangement. The mean blood glucose was 28.4 ± 12.1 mmol/L (range 14.2-68.4 mmol/L). Mean arterial pH was 7.18 ± 0.14, with serum bicarbonate of 10.2 ± 4.8 mmol/L. The mean anion gap was 24.6 ± 6.2 mmol/L. Acute kidney injury, defined as serum creatinine ≥1.5 times baseline, was present in 68 patients (36.8%) on admission.
Table 5. Laboratory Parameters at Presentation (N=185)
| Parameter | Mean ± SD | Median (IQR) |
|---|---|---|
| Blood glucose (mmol/L) | 28.4 ± 12.1 | 20.1-34.8 |
| Arterial pH | 7.18 ± 0.14 | 7.08-7.28 |
| Serum bicarbonate (mmol/L) | 10.2 ± 4.8 | 6.8-13.5 |
| Anion gap (mmol/L) | 24.6 ± 6.2 | 20.0-28.5 |
| Serum sodium (mmol/L) | 132 ± 6.4 | 128-137 |
| Serum potassium (mmol/L) | 5.1 ± 1.2 | 4.2-5.9 |
| Serum creatinine (μmol/L) | 168 ± 98 | 98-218 |
| Blood urea (mmol/L) | 12.4 ± 8.2 | 6.8-15.6 |
| Serum ketones (mmol/L) | 5.8 ± 2.4 | 4.0-7.2 |
| HbA1c (%) | 11.8 ± 2.6 | 10.0-13.5 |
| White cell count (×10⁹/L) | 14.2 ± 6.8 | 9.4-17.6 |
| C-reactive protein (mg/L) | 86 ± 72 | 24-128 |
4.4 DKA Severity
According to ADA severity criteria, 42 patients (22.7%) had mild DKA, 85 patients (45.9%) had moderate DKA, and 58 patients (31.4%) had severe DKA (Figure 3). Patients with severe DKA were older (mean age 48.2 vs 39.8 years, p=0.002), more likely to have infection as a precipitant (51.7% vs 33.1%, p=0.02), and had higher rates of altered mental status (48.3% vs 10.2%, p<0.001).
4.5 Precipitating Factors
Precipitating factors are summarised in Table 6 and Figure 4. Infection was the most common precipitant, identified in 72 patients (38.9%). Among patients with infection, the most common sites were urinary tract (28 patients, 38.9%), respiratory (24 patients, 33.3%), and skin/soft tissue (12 patients, 16.7%).
Table 6. Precipitating Factors for DKA Episodes (N=185)
| Precipitating Factor | n | % |
|---|---|---|
| Infection | 72 | 38.9% |
| Non-adherence | 48 | 25.9% |
| New-onset DM | 35 | 18.9% |
| Unknown | 18 | 9.7% |
| Alcohol | 8 | 4.3% |
| Other | 4 | 2.2% |
Medication non-adherence was the second most common precipitant (48 patients, 25.9%). Reasons for non-adherence included medication stockouts (14 patients), financial constraints (12 patients), deliberate omission (10 patients), and other/unspecified reasons (12 patients). New-onset diabetes accounted for 18.9% of presentations. In 18 patients (9.7%), no precipitant could be identified despite thorough investigation.
4.6 Management and Outcomes
All patients received intravenous fluids and insulin infusion according to institutional protocol. ICU admission was required in 58 patients (31.4%), predominantly those with severe DKA or haemodynamic instability. Mechanical ventilation was required in 14 patients (7.6%). The mean length of hospital stay was 5.2 ± 3.8 days (median 4 days, IQR 3-6 days).
Table 7. Clinical Outcomes by DKA Severity
| Outcome | Mild (n=42) | Moderate (n=85) | Severe (n=58) | p-value |
|---|---|---|---|---|
| ICU admission, n (%) | 2 (4.8%) | 18 (21.2%) | 38 (65.5%) | <0.001 |
| Mechanical ventilation, n (%) | 0 (0%) | 4 (4.7%) | 10 (17.2%) | <0.001 |
| Length of stay (days), mean ± SD | 3.2 ± 1.8 | 4.6 ± 2.9 | 7.8 ± 4.2 | <0.001 |
| In-hospital mortality, n (%) | 0 (0%) | 3 (3.5%) | 10 (17.2%) | <0.001 |
| 30-day recurrence, n (%) | 4 (9.5%) | 5 (5.9%) | 3 (5.2%) | 0.62 |
4.7 Mortality Analysis
Overall in-hospital mortality was 7.0% (13/185 patients). Mortality was significantly higher in patients with severe DKA (17.2%) compared to those with mild or moderate DKA (2.4%; p<0.001). The comparison between survivors and non-survivors is presented in Table 8.
Table 8. Comparison of Survivors vs Non-Survivors
| Variable | Survivors (n=172) | Non-survivors (n=13) | p-value |
|---|---|---|---|
| Age (years), mean ± SD | 41.2 ± 14.8 | 55.6 ± 12.4 | <0.001 |
| Female sex, n (%) | 99 (57.6%) | 8 (61.5%) | 0.78 |
| Type 2 DM, n (%) | 88 (51.2%) | 10 (76.9%) | 0.07 |
| Severe DKA, n (%) | 48 (27.9%) | 10 (76.9%) | <0.001 |
| pH, mean ± SD | 7.20 ± 0.12 | 7.02 ± 0.16 | <0.001 |
| GCS <15, n (%) | 32 (18.6%) | 9 (69.2%) | <0.001 |
| Infection, n (%) | 62 (36.0%) | 10 (76.9%) | 0.004 |
| AKI on admission, n (%) | 58 (33.7%) | 10 (76.9%) | 0.002 |
| ICU admission, n (%) | 45 (26.2%) | 13 (100%) | <0.001 |
| Mechanical ventilation, n (%) | 4 (2.3%) | 10 (76.9%) | <0.001 |
4.8 Predictors of Mortality
On multivariate logistic regression analysis, independent predictors of mortality included severe DKA (OR 4.2; 95% CI 1.8-9.8; p=0.001), age ≥60 years (OR 3.1; 95% CI 1.2-7.9; p=0.018), GCS <15 on admission (OR 5.8; 95% CI 2.1-16.2; p<0.001), and acute kidney injury on admission (OR 3.6; 95% CI 1.4-9.2; p=0.008). The presence of infection as a precipitant showed a trend towards increased mortality (OR 2.4; 95% CI 0.9-6.4; p=0.082) but did not reach statistical significance.
Table 9. Independent Predictors of Mortality on Multivariate Logistic Regression
| Variable | Odds Ratio | 95% CI | p-value |
|---|---|---|---|
| Severe DKA (pH <7.0) | 4.2 | 1.8-9.8 | 0.001 |
| Age ≥60 years | 3.1 | 1.2-7.9 | 0.018 |
| GCS <15 on admission | 5.8 | 2.1-16.2 | <0.001 |
| Infection as precipitant | 2.4 | 0.9-6.4 | 0.082 |
| AKI on admission | 3.6 | 1.4-9.2 | 0.008 |
| Mechanical ventilation | 18.4 | 3.4-99.2 | <0.001 |
Model adjusted for age, sex, diabetes type, DKA severity, infection, GCS, and AKI. Hosmer-Lemeshow goodness-of-fit p=0.68.
Chapter 5: Discussion
5.1 Summary of Main Findings
This study provides contemporary data on the clinical profile and outcomes of DKA at a tertiary hospital in Gauteng Province. Among 185 patients with confirmed DKA, the overall in-hospital mortality was 7.0%. Key findings include the predominance of Type 2 diabetes (53.0%), infection (38.9%) and non-adherence (25.9%) as leading precipitants, and the identification of severe DKA, advanced age, altered consciousness, and acute kidney injury as independent predictors of mortality.
5.2 Mortality Rate
The 7.0% mortality rate observed in our study is substantially higher than contemporary rates reported from high-income countries, where case fatality rates are typically less than 1%.[22][23] However, our mortality rate is consistent with other African studies. Ndebele and Naidoo reported 8.9% mortality in a rural KwaZulu-Natal hospital,[26] while Mahlangu et al. documented 7.2% mortality at a Johannesburg academic hospital.[28] Pepper and colleagues reported 9.4% mortality at a Cape Town secondary-level hospital.[27]
The higher mortality in resource-limited settings likely reflects multiple factors. Late presentation is common, with patients arriving with more severe metabolic derangement than typically seen in high-income settings. In our cohort, nearly one-third of patients presented with severe DKA (pH <7.0), compared to 10-20% in developed country studies.[12] Limited critical care capacity may also contribute, with delays in ICU admission potentially impacting outcomes in the most severely ill patients.
5.3 Demographic Profile
The mean age of 42.3 years and female predominance (57.8%) are consistent with regional data. Ndebele and Naidoo reported median age 38 years with 64% female patients,[26] while Ogbera et al. from Nigeria found mean age 44 years with 56% female predominance.[31] The female predominance may reflect the higher overall diabetes prevalence in women in South Africa,[39] as well as potential gender differences in healthcare-seeking behaviour.
5.4 Type 2 Diabetes in DKA
The predominance of Type 2 diabetes (53.0%) in our cohort challenges the traditional teaching that DKA is primarily a complication of Type 1 diabetes. This finding aligns with data from other African studies showing that Type 2 diabetes accounts for 40-60% of DKA presentations in sub-Saharan Africa.[29][33] This pattern reflects the high prevalence of ketosis-prone Type 2 diabetes in populations of African descent, characterised by acute presentations with ketosis but subsequent beta-cell recovery allowing discontinuation of insulin therapy.[36][38]
Clinicians should maintain a high index of suspicion for DKA in all patients with diabetes, regardless of type. The distinction between Type 1 and Type 2 diabetes may be difficult at initial presentation, and definitive classification often requires follow-up assessment of beta-cell function and autoantibody status.
5.5 Precipitating Factors
Infection was the most common precipitating factor (38.9%), consistent with global literature.[2][51] The high rate of urinary tract and respiratory infections underscores the importance of systematic infection screening and early empiric antibiotic therapy in DKA management. The presence of infection also independently predicted mortality, highlighting its prognostic significance.
Medication non-adherence (25.9%) represents a potentially modifiable risk factor. The reasons identified—stockouts, financial constraints, and deliberate omission—suggest that multifaceted interventions addressing both supply-side and demand-side barriers are needed. Diabetes education, simplified regimens, and addressing structural barriers to medication access should be priorities.
The proportion of new-onset diabetes (18.9%) is notable and aligns with regional data. This finding has implications for public health, suggesting that community awareness campaigns and improved screening could identify patients before they present with acute decompensation.
5.6 Predictors of Mortality
The identification of severe DKA, advanced age, altered consciousness, and acute kidney injury as independent predictors of mortality is consistent with international literature.[71][72] These findings have practical implications for clinical care, allowing early identification of high-risk patients who may benefit from more intensive monitoring and aggressive intervention.
The strong association between mechanical ventilation and mortality (OR 18.4) reflects the severity of illness requiring ventilatory support rather than a causal relationship. This finding underscores the importance of early recognition and treatment to prevent progression to respiratory failure.
5.7 Strengths and Limitations
Strengths: This study provides contemporary local data to inform clinical practice and resource allocation. The sample size exceeded the minimum required for the primary objective, and comprehensive clinical data were available for most patients. The use of standardised definitions for DKA and its complications enhances comparability with other studies.
Limitations: The retrospective design carries inherent limitations including potential selection bias and reliance on documented information. Single-centre data may not be generalisable to other settings, particularly primary and secondary level facilities. Some variables, such as socioeconomic status, duration of symptoms before presentation, and long-term outcomes, were not consistently documented and could not be analysed. The relatively small number of deaths limits the precision of estimates for mortality predictors and precludes development of a robust prognostic model.
Chapter 6: Conclusion and Recommendations
6.1 Conclusion
Diabetic ketoacidosis remains a significant cause of morbidity and mortality at our institution. The 7.0% in-hospital mortality rate, while higher than high-income country benchmarks, is consistent with regional African data and comparable to other South African tertiary centres. Infection and medication non-adherence are the predominant precipitating factors, both of which are potentially modifiable through targeted interventions.
The high proportion of Type 2 diabetes (53.0%) and new-onset presentations (18.9%) challenges traditional paradigms and highlights the changing epidemiology of DKA in our setting. Identification of high-risk patients—those with severe DKA, advanced age, altered consciousness, or acute kidney injury—allows for risk stratification and appropriate resource allocation.
6.2 Recommendations
For Clinical Practice:
- Implement early risk stratification using identified predictors to triage high-risk patients for ICU-level care
- Maintain vigilance for DKA in all patients with diabetes, regardless of type
- Ensure systematic infection screening with early empiric antibiotic therapy where indicated
- Standardise DKA management protocols aligned with international guidelines
- Implement DKA prevention education at discharge for all patients
For Public Health and Policy:
- Develop community education programmes to improve diabetes awareness and recognition of DKA symptoms
- Address barriers to medication adherence, including cost, supply, and health literacy
- Enhance diabetes screening programmes to identify patients before first presentation as DKA
- Strengthen referral pathways between primary and tertiary care facilities
For Future Research:
- Prospective multicentre studies to better characterise risk factors and validate prognostic models
- Implementation research on adherence interventions and prevention programmes
- Development and validation of context-appropriate DKA severity scores
- Economic analyses to guide resource allocation and demonstrate value of prevention
- Long-term follow-up studies to assess recurrence rates and factors influencing outcomes
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