Seventy-Two Hour Mortality Prediction Model in Patients with Diabetic Ketoacidosis: A Retrospective Cohort Study

Impact of Charlson Comorbidity Index on in-hospital mortality of patients with hyperglycemic crises: A propensity score matching analysis

AIM

This study was designed to investigate the association between Charlson Comorbidity Index (CCI) and in-hospital mortality and other clinical outcomes among patients with hyperglycemic crises.

METHOD

This retrospective cohort study was conducted using data from electric medical records. A total of 1668 diabetic patients with hyperglycemic crises from six tertiary hospitals met the inclusion criteria. CCI < 4 was defined as low CCI and CCI ≥ 4 was defined as high CCI. Propensity score matching (PSM) with the 1:1 nearest neighbour matching method and the caliper value of 0.02 was used to match the baseline characteristics of patients with high CCI and low CCI to reduce the confounding bias. In-hospital mortality, ICU admission, hypoglycemia, hypokalemia, acute kidney injury, length of stay (LOS), and hospitalisation expense between low CCI and high CCI were compared and assessed. Univariate and multivariate regression were applied to estimate the impact of CCI on in-hospital and other clinical outcomes.

OUTCOME

One hundred twenty-one hyperglycemic crisis (HC) patients died with a mortality rate of 7.3%. After PSM, compared with low CCI, patients with high CCI suffered higher in-hospital mortality, ICU admission, LOS, and hospitalisation expenses. After multivariate regression, age (aOR: 1.12, 95% confidence interval [CI]: 1.06-1.18, p < 0.001), CCI(aOR: 4.42, 95% CI: 1.56-12.53, p = 0.005), uninsured (aOR: 22.32, 95% CI: 4.26-116.94, p < 0.001), shock (aOR: 10.57, 95% CI: 1.41-79.09, p = 0.022), mechanical ventilation (aOR: 75.29, 95% CI: 12.37-458.28, p < 0.001), and hypertension (aOR: 4.34, 95% CI: 1.37-13.82, p = 0.013) were independent risk factors of in-hospital mortality of HC patients. Besides, high CCI was an independent risk factor for higher ICU Admission (aOR: 5.91, 95% CI: 2.31-15.08, p < 0.001), hypoglycemia (aOR: 2.19, 95% CI:1.01-4.08, p = 0.049), longer LOS (aOR: 1.23, 95% CI: 1.19-2.27, p = 0.021), and higher hospitalisation expense (aOR: 2089.97, 95% CI: 193.33-3988.61, p = 0.031) of HC patients.

CONCLUSION

CCI is associated with in-hospital mortality, ICU admission, hypoglycemia, LOS, and hospitalisation expense of HC patients. CCI could be an ideal indicator to identify, monitor, and manage chronic comorbidities among HC patients.

Associations between Late Lactate Clearance and Clinical Outcomes in Adults with Hyperlactataemia in the Setting of Diabetic Ketoacidosis

Objective: This study aimed to determine the associations between lactate clearance in hyperlactataemic patients with diabetic ketoacidosis (DKA) and intensive care unit (ICU), hospital length of stay (LOS), and case-fatality. Methods: A retrospective, multicentre, cohort study of adult patients admitted to ICU with hyperlactataemia and a primary diagnosis of DKA from twelve sites in Queensland, Australia was conducted utilising pre-existing datasets that were linked for research purposes. The patients were divided into early and late lactate clearance groups; the early lactate clearance group included patients whose lactate returned to <2.0 mmol/L within 12 h, and the remainder were classified as late lactate clearance group. Results: The final dataset included 511 patients, 427 in the early lactate clearance group and 84 in the late lactate clearance group. Late lactate clearance was associated with increasing ICU LOS (β = +15.82, 95% CI +0.05 to +31.59, p < 0.049), increasing hospital LOS (β = +7.24, 95% CI +0.11 to 14.37, p = 0.048) and increasing Acute Physiology and Chronic Health Evaluation(APACHE) III score (ICU LOS outcome variable β = +1.05, 95% CI +0.88 to +1.22, p < 0.001; hospital LOS outcome variable β = +3.40, 95% CI +2.22 to 4.57, p < 0.001). Hospital case-fatality was not significantly different (2.2% in the early clearance group vs. 1.7% in the late clearance group, p = 0.496). Conclusions: In hyperlactataemic patients with DKA, late lactate clearance was associated with a statistically significant increase in both ICU and hospital LOS, though the clinical significance in both is minor.

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.

Projection of diabetes morbidity and mortality till 2045 in Indonesia based on risk factors and NCD prevention and control programs

Diabetes Mellitus is one of the biggest health problems in Indonesia but the research on the disease's projection is still limited. This study aimed to make a projection model of prevalence and mortality of diabetes in Indonesia based on risk factors and NCD programs. The study was a quantitative non-experimental study through multiple linear regression models and system dynamics. The baseline projection was created by 2018 data and projections until 2045 involved the dynamization of risk factors and programs, population, and case fatality rate. The model was created from 205 districts data. This study used secondary data from Basic Health Research, BPJS Kesehatan, NCD programs, and Ministry of Health. The prevalence of diabetes in Indonesia is estimated to increase from 9.19% in 2020 (18.69 million cases) to 16.09% in 2045 (40.7 million cases). The prevalence will be lower to 15.68% (39.6 million) if interventions of programs were carried out, and to 9.22% (23.2 million) if the programs were added with prevention of risk factors. The projected number of deaths due to diabetes increases from 433,752 in 2020 to 944,468 in 2045. Deaths due to stroke among diabetes increases from 52,397 to 114,092 in the same period. Deaths from IHD among diabetes increase from 35,351 to 76,974, and deaths from chronic kidney disease among diabetes increase from 29,061 to 63,279. Diabetes prevalence and mortality in Indonesia rise significantly in Indonesia and can be reduced by intervention of several programs and risk factors. This study findings could be source of planning and evaluation of Diabetes prevention and control program at national and provincial level in the future related to risk factors control and program development.

Clinical nomogram prediction model to assess the risk of prolonged ICU length of stay in patients with diabetic ketoacidosis: a retrospective analysis based on the MIMIC-IV database

BACKGROUND

The duration of hospitalization, especially in the intensive care unit (ICU), for patients with diabetic ketoacidosis (DKA) is influenced by patient prognosis and treatment costs. Reducing ICU length of stay (LOS) in patients with DKA is crucial for optimising healthcare resources utilization. This study aimed to establish a nomogram prediction model to identify the risk factors influencing prolonged LOS in ICU-managed patients with DKA, which will serve as a basis for clinical treatment, healthcare safety, and quality management research.

METHODS

In this single-centre retrospective cohort study, we performed a retrospective analysis using relevant data extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Clinical data from 669 patients with DKA requiring ICU treatment were included. Variables were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) binary logistic regression model. Subsequently, the selected variables were subjected to a multifactorial logistic regression analysis to determine independent risk factors for prolonged ICU LOS in patients with DKA. A nomogram prediction model was constructed based on the identified predictors. The multivariate variables included in this nomogram prediction model were the Oxford acute severity of illness score (OASIS), Glasgow coma scale (GCS), acute kidney injury (AKI) stage, vasoactive agents, and myocardial infarction.

RESULTS

The prediction model had a high predictive efficacy, with an area under the curve value of 0.870 (95% confidence interval [CI], 0.831-0.908) in the training cohort and 0.858 (95% CI, 0.799-0.916) in the validation cohort. A highly accurate predictive model was depicted in both cohorts using the Hosmer-Lemeshow (H-L) test and calibration plots.

CONCLUSION

The nomogram prediction model proposed in this study has a high clinical application value for predicting prolonged ICU LOS in patients with DKA. This model can help clinicians identify patients with DKA at risk of prolonged ICU LOS, thereby enhancing prompt intervention and improving prognosis.

Clinical profile of patients with diabetic ketoacidosis and hyperglycemic hyperosmolar syndrome in Japan: a multicenter retrospective cohort study

INTRODUCTION

Diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar syndrome (HHS) are life-threatening complications of diabetes mellitus. Their clinical profiles have not been fully investigated.

METHODS

A multicenter retrospective cohort study was conducted in 21 acute care hospitals in Japan. Patients included were adults aged 18 or older who had been hospitalized from January 1, 2012, to December 31, 2016 due to DKA or HHS. The data were extracted from patient medical records. A four-group comparison (mild DKA, moderate DKA, severe DKA, and HHS) was performed to evaluate outcomes.

RESULTS

A total of 771 patients including 545 patients with DKA and 226 patients with HHS were identified during the study period. The major precipitating factors of disease episodes were poor medication compliance, infectious diseases, and excessive drinking of sugar-sweetened beverages. The median hospital stay was 16 days [IQR 10-26 days]. The intensive care unit (ICU) admission rate was 44.4% (mean) and the rate at each hospital ranged from 0 to 100%. The in-hospital mortality rate was 2.8% in patients with DKA and 7.1% in the HHS group. No significant difference in mortality was seen among the three DKA groups.

CONCLUSIONS

The mortality rate of patients with DKA in Japan is similar to other studies, while that of HHS was lower. The ICU admission rate varied among institutions. There was no significant association between the severity of DKA and mortality in the study population.

TRIAL REGISTRATION

This study is registered in the UMIN clinical Trial Registration System (UMIN000025393, Registered 23th December 2016).

Association between elevated lactate and clinical outcomes in adults with diabetic ketoacidosis

PURPOSE

To assess the occurrence of hyperlactatemia among patients admitted to the intensive care unit (ICU) with diabetic ketoacidosis (DKA), and effect on in-hospital mortality.

MATERIALS AND METHODS

A retrospective, multicentre, cohort study of adult patients admitted to ICU with a primary diagnosis of DKA in Australia and New Zealand, utilising a pre-existing dataset. The primary exposure variable was lactate, dichotomised into normolactatemia (lactate <2.0 mmol/L) and hyperlactatemia (lactate ≥ 2.0 mmol/L) groups. The primary outcome was in-hospital mortality. Secondary outcomes included ICU and hospital length of stay (LOS), requirement for ventilation, renal replacement therapy (RRT) and inotropes.

RESULTS

The final dataset included 9061 patients. Hyperlactatemia was associated with in-hospital mortality (Odds Ratio [OR] 1.785 (95% CI 1.122-2.841, p = 0.014), hospital LOS (Geometric mean ratio [GMR] 1.063, 95% CI 1.025-1.103, p = 0.001), ICU LOS (GMR 1.057, 95% CI 1.026-1.09. p < 0.001), RRT (OR 2.198, 95% CI 1.449-3.334, p < 0.001) and inotropes (OR 1.578, 95% CI 1.311-1.899, p < 0.001). These associations persisted in Type 2 but not Type 1 diabetics.

CONCLUSIONS

Hyperlactatemia in patients admitted to ICU with DKA is associated with higher mortality, longer hospital and ICU LOS, and higher rates of mechanical ventilation, RRT and inotropes.

Clinical and biochemical predictors of intensive care unit admission among patients with diabetic ketoacidosis

BACKGROUND

Diabetic ketoacidosis (DKA) contributes to 94% of diabetes-related hospital admissions, and its incidence is rising. Due to the complexity of its management and the need for rigorous monitoring, many DKA patients are managed in the intensive care unit (ICU). However, studies comparing DKA patients managed in ICU to non-ICU settings show an increase in healthcare costs without significantly affecting patient outcomes. It is, therefore, essential to identify suitable candidates for ICU care in DKA patients.

AIM

To evaluate factors that predict the requirement for ICU care in DKA patients.

METHODS

This retrospective study included consecutive patients with index DKA episodes who presented to the emergency department of four general hospitals of Hamad Medical Corporation, Doha, Qatar, between January 2015 and March 2021. All adult patients (> 14 years) fulfilling the American Diabetes Association criteria for DKA diagnosis were included.

RESULTS

We included 922 patients with DKA in the final analysis, of which 229 (25%) were managed in the ICU. Compared to non-ICU patients, patients admitted to ICU were older [mean (SD) age of 40.4 ± 13.7 years vs 34.5 ± 14.6 years; P < 0.001], had a higher body mass index [median (IQR) of 24.6 (21.5-28.4) kg/m² vs 23.7 (20.3-27.9) kg/m²; P < 0.030], had T2DM (61.6%) and were predominantly males (69% vs 31%; P < 0.020). ICU patients had a higher white blood cell count [median (IQR) of 15.1 (10.2-21.2) × 10³/uL vs 11.2 (7.9-15.7) × 10³/uL, P < 0.001], urea [median (IQR) of 6.5 (4.6-10.3) mmol/L vs 5.6 (4.0-8.0) mmol/L; P < 0.001], creatinine [median (IQR) of 99 (75-144) mmol/L vs 82 (63-144) mmol/L; P < 0.001], C-reactive protein [median (IQR) of 27 (9-83) mg/L vs 14 (5-33) mg/L; P < 0.001] and anion gap [median (IQR) of 24.0 (19.2-29.0) mEq/L vs 22 (17-27) mEq/L; P < 0.001]; while a lower venous pH [mean (SD) of 7.10 ± 0.15 vs 7.20 ± 0.13; P < 0.001] and bicarbonate level [mean (SD) of 9.2 ± 4.1 mmol/L vs 11.6 ± 4.3 mmol/L; P < 0.001] at admission than those not requiring ICU management of DKA (P < 0.001). Patients in the ICU group had a longer LOS [median (IQR) of 4.2 (2.7-7.1) d vs 2.0 (1.0-3.9) d; P < 0.001] and DKA duration [median (IQR) of 24 (13-37) h vs 15 (19-24) h, P < 0.001] than those not requiring ICU admission. In the multivariate logistic regression analysis model, age, Asian ethnicity, concurrent coronavirus disease 2019 (COVID-19) infection, DKA severity, DKA trigger, and NSTEMI were the main predicting factors for ICU admission.

CONCLUSION

In the largest tertiary center in Qatar, 25% of all DKA patients required ICU admission. Older age, T2DM, newly onset DM, an infectious trigger of DKA, moderate-severe DKA, concurrent NSTEMI, and COVID-19 infection are some factors that predict ICU requirement in a DKA patient.

Prediction of 72-hour mortality in patients with extremely high serum C-reactive protein levels using a novel weighted average of risk scores

The risk factors associated with mortality in patients with extremely high serum C-reactive protein (CRP) levels are controversial. In this retrospective single-center cross-sectional study, the clinical and laboratory data of patients with CRP levels ≥40 mg/dL treated in Saitama Medical Center, Japan from 2004 to 2017 were retrieved from medical records. The primary outcome was defined as 72-hour mortality after the final CRP test. Forty-four mortal cases were identified from the 275 enrolled cases. Multivariate logistic regression analysis (MLRA) was performed to explore the parameters relevant for predicting mortality. As an alternative method of prediction, we devised a novel risk predictor, “weighted average of risk scores” (WARS). WARS features the following: (1) selection of candidate risk variables for 72-hour mortality by univariate analyses, (2) determination of C-statistics and cutoff value for each variable in predicting mortality, (3) 0–1 scoring of each risk variable at the cutoff value, and (4) calculation of WARS by weighted addition of the scores with weights assigned according to the C-statistic of each variable. MLRA revealed four risk variables associated with 72-hour mortality—age, albumin, inorganic phosphate, and cardiovascular disease—with a predictability of 0.829 in C-statistics. However, validation by repeated resampling of the 275 records showed that a set of predictive variables selected by MLRA fluctuated occasionally because of the presence of closely associated risk variables and missing data regarding some variables. WARS attained a comparable level of predictability (0.837) by combining the scores for 10 risk variables, including age, albumin, electrolytes, urea, lactate dehydrogenase, and fibrinogen. Several mutually related risk variables are relevant in predicting 72-hour mortality in patients with extremely high CRP levels. Compared to conventional MLRA, WARS exhibited a favorable performance with flexible coverage of many risk variables while allowing for missing data.