Clinical Profile and Outcomes of Adult Patients with Diabetic Ketoacidosis at a Tertiary Hospital in Gauteng Province
Principal Investigator:
[Candidate Name]
Student Number:
[Student Number]
[University Name]
Faculty of Health Sciences
Department of Internal Medicine
Supervisor:
[Supervisor Name]
Protocol Version: 1.0
Date: [Month Year]
Synopsis
Table 1: Protocol Synopsis
| Element | Description |
|---|---|
| Study Title | Clinical Profile and Outcomes of Adult Patients with Diabetic Ketoacidosis at a Tertiary Hospital in Gauteng Province |
| Study Design | Retrospective descriptive cross-sectional study with analytical components |
| Study Setting | [Hospital Name], Gauteng Province |
| Study Period | January 2022 - December 2023 (24 months) |
| Study Population | Adults (≥18 years) admitted with DKA |
| Sample Size | Minimum 126 patients (target: 160-200) |
| Primary Outcome | In-hospital mortality |
| Secondary Outcomes | ICU admission, length of stay, complications |
| Ethics | HREC approval to be obtained; consent waiver requested |
1. Background and Literature Review
1.1 Introduction
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 condition requires prompt recognition and aggressive management with intravenous fluids, insulin therapy, and electrolyte replacement.[4]
1.2 Epidemiology
The global burden of diabetes mellitus has increased dramatically over recent decades. 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.[13] 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.[13]
The incidence of DKA varies substantially across populations. 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 (T1DM).[6] In the United States, emergency department visits for DKA among adults with T1DM increased by 54.2% between 2006 and 2017.[7]
1.3 South African Context
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.[17] 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.[9]
Previous South African studies have documented DKA mortality rates between 5-15%, substantially higher than rates below 1% reported in high-income settings.[8][10] Ndebele and Naidoo from a district hospital in KwaZulu-Natal reported 9.4% mortality, with infection as the precipitating factor in 45% of cases.[8]
1.4 Problem Statement
While DKA mortality has declined to less than 1% in many high-income settings,[14] rates in resource-limited settings remain substantially higher, ranging from 5-30% in various African studies.[11][12] 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. This knowledge gap hampers efforts to identify modifiable risk factors, develop prognostic tools, and design targeted interventions to improve DKA outcomes.
1.5 Justification
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.
2. Aim and Objectives
2.1 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?
PICO Framework
- Population: Adult patients (≥18 years) admitted with diabetic ketoacidosis
- Intervention/Exposure: Clinical characteristics, precipitating factors, management received
- Comparison: Known vs newly diagnosed diabetes; Type 1 vs Type 2; ICU vs ward
- Outcomes: ICU admission, length of stay, mortality, DKA resolution time
2.2 Aim
To determine the clinical profile, precipitating factors, and outcomes of adult patients admitted with diabetic ketoacidosis at [Hospital Name] over a two-year period (January 2022 to December 2023).
2.3 Primary Objective
To determine the incidence and in-hospital mortality rate of diabetic ketoacidosis among adult patients at [Hospital Name].
2.4 Secondary Objectives
- To describe the demographic characteristics of patients presenting with DKA, including age, sex, and area of residence.
- To describe the clinical characteristics at presentation, including severity classification, vital signs, and laboratory parameters.
- To determine the proportion of patients with newly diagnosed diabetes presenting as DKA versus those with known diabetes.
- To characterise the diabetes type (Type 1 vs Type 2) and prior diabetes management among patients with DKA.
- To identify the precipitating factors for DKA episodes, including infection, insulin omission, and other triggers.
- To assess the management received, including fluid resuscitation, insulin protocols, and adherence to guidelines.
- To determine factors associated with ICU admission and in-hospital mortality.
- To describe the clinical outcomes, including DKA resolution time, length of hospital stay, and complications.
3. Methodology
3.1 Study Design
Retrospective descriptive cross-sectional study with analytical components.
A retrospective design is appropriate because DKA is a relatively uncommon presentation requiring review of historical cases, patient records contain comprehensive clinical data for analysis, the design is feasible within the time constraints of an MMed thesis, and it allows efficient identification of a large cohort.
3.2 Study Setting
[Hospital Name] is a tertiary academic hospital located in [City], Gauteng Province, affiliated with the [University] School of Medicine. The hospital serves a catchment population of approximately [X] million people.
The study will be conducted in the Department of Internal Medicine, with data from the Medical Emergency Unit, Medical Wards, Intensive Care Unit, and High Care Unit.
3.3 Study Period
January 1, 2022 to December 31, 2023 (24 months)
3.4 Study Population
Inclusion Criteria
- Age ≥18 years at time of admission
- Diagnosis of DKA meeting biochemical criteria:
- Blood glucose >13.9 mmol/L (250 mg/dL)
- Arterial pH <7.30 OR venous pH <7.25
- Serum bicarbonate <18 mEq/L
- Presence of ketonemia (β-hydroxybutyrate ≥3.0 mmol/L) OR ketonuria
- Admitted to medical services during the study period
- Medical records available for review
Exclusion Criteria
- Patients presenting with isolated hyperosmolar hyperglycaemic state (HHS) without ketoacidosis
- Patients transferred to other facilities before DKA resolution
- Incomplete records precluding determination of DKA diagnosis or outcomes
- Pregnancy-related DKA (excluded for population homogeneity)
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 is 126 patients. Based on hospital records showing approximately 80-100 DKA admissions annually, the two-year study period should yield approximately 160-200 patients, exceeding the minimum requirement.
3.6 Data Collection
Patients will be identified through ICD-10 diagnosis codes (E10.1, E11.1, E13.1, E14.1), emergency unit admission registers, and ICU admission logs. A structured data collection form will be used to extract information on demographics, diabetes history, presentation, laboratory parameters, precipitants, management, and outcomes.
4. Variables and Definitions
Table 2: Primary and Secondary Outcome Variables
| Variable | Type | Definition |
|---|---|---|
| In-hospital mortality | Binary | Death during index admission |
| ICU admission | Binary | Admission to ICU during stay |
| Length of stay | Continuous | Days from admission to discharge |
| DKA resolution time | Continuous | Hours from insulin start to biochemical resolution |
| Complications | Categorical | Hypoglycaemia, hypokalaemia, AKI, cerebral oedema |
Table 3: Key Variable Definitions
| Variable | Operational 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 |
| Acute Kidney Injury | Serum creatinine ≥1.5× baseline or ≥26.5 μmol/L increase within 48 hours |
| Infection | Clinical or microbiological evidence of infection at presentation |
| Non-adherence | Documented insulin omission or irregular use prior to admission |
| New-onset diabetes | No prior diagnosis of diabetes before the DKA episode |
| DKA resolution | pH >7.3, bicarbonate >18 mmol/L, anion gap ≤12 mmol/L |
5. Statistical Analysis Plan
All analyses will be performed using STATA 17 (StataCorp, College Station, TX) with statistical significance defined as p < 0.05.
5.1 Descriptive Statistics
- Continuous variables: Mean (SD) if normally distributed; median (IQR) if skewed
- Categorical variables: Frequencies and percentages with 95% CI
- Normality assessment: Shapiro-Wilk test and visual inspection of histograms
5.2 Inferential Statistics
Table 4: Statistical Tests by Analysis Type
| Analysis | Variables | Statistical Test |
|---|---|---|
| Compare groups (continuous, normal) | Age by mortality | Independent t-test |
| Compare groups (continuous, skewed) | LOS by ICU admission | Mann-Whitney U test |
| Compare proportions | Mortality by severity | Chi-square or Fisher's exact |
| Identify associations | Factors for mortality | Logistic regression |
5.3 Multivariate Analysis
Logistic regression will be performed to identify independent predictors of mortality. Variables with p < 0.2 on univariate analysis will be included. Model fit will be assessed using the Hosmer-Lemeshow goodness-of-fit test. Results will be presented as adjusted odds ratios with 95% confidence intervals.
5.4 Missing Data
Complete case analysis will be used if missing data <5%. For missing data 5-20%, multiple imputation will be considered. Outcome variables will not be imputed.
6. Ethical Considerations
6.1 Ethics Approval
Ethics approval will be sought from the [University] Human Research Ethics Committee (HREC) prior to commencement of the study.
6.2 Informed Consent
Waiver of informed consent will be requested based on the following justifications:
- This is a retrospective study using existing medical records
- The study poses minimal risk to participants
- It is impracticable to obtain consent from all participants given the retrospective nature
- The research could not otherwise be conducted
6.3 Confidentiality
- Unique study identification numbers will be used (not hospital numbers)
- No identifying information will be stored in the analysis database
- Data will be stored securely on password-protected devices
- Only aggregate data will be reported in publications
6.4 Institutional Permission
Permission will be obtained from the Hospital CEO/Medical Manager, Head of Internal Medicine, and Provincial Health Research Committee as required.
6.5 Potential Risks and Benefits
Risks: Minimal risk. There is no direct patient contact. Risk of breach of confidentiality will be minimised through de-identification of data.
Benefits: No direct benefit to individual participants. The study will generate knowledge that may improve the care of future patients with DKA.
7. Timeline and Budget
Table 5: Study Timeline
| Phase | Activity | Duration |
|---|---|---|
| 1 | Protocol development, ethics submission | 2 months |
| 2 | Ethics approval, institutional permissions | 2 months |
| 3 | Data collection | 3 months |
| 4 | Data cleaning and analysis | 2 months |
| 5 | Thesis writing | 3 months |
| 6 | Revision and submission | 2 months |
| Total | 14 months |
Table 6: Budget Estimate
| Item | Description | Estimated Cost (ZAR) |
|---|---|---|
| Ethics application | HREC submission fee | 1,500 |
| Stationery | Data collection forms, printing | 500 |
| Statistical software | STATA license (if not institutional) | 0* |
| Statistical consultation | External statistician review | 3,000 |
| Binding and submission | Thesis printing and binding | 1,500 |
| Total | 6,500 |
*Institutional license available
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