Background: Increasing incidence of dengue cases in Malaysia over the last few years has been paralleled by increased deaths. Mortality prediction models will therefore be useful in clinical management. The aim of this study is to identify factors at diagnosis of severe dengue that predicts mortality and assess predictive models based on these identified factors. Method: This is a retrospective cohort study of confirmed severe dengue patients that were admitted in 2014 to Hospital Kuala Lumpur. Data on baseline characteristics, clinical parameters, and laboratory findings at diagnosis of severe dengue were collected. The outcome of interest is death among patients diagnosed with severe dengue. Results: There were 199 patients with severe dengue included in the study. Multivariate analysis found lethargy, OR 3.84 (95% CI 1.23–12.03); bleeding, OR 8.88 (95% CI 2.91–27.15); pulse rate, OR 1.04 (95% CI 1.01–1.07); serum bicarbonate, OR 0.79 (95% CI 0.70–0.89) and serum lactate OR 1.27 (95% CI 1.09–1.47), to be statistically significant predictors of death. The regression equation to our model with the highest AUROC, 83.5 (95% CI 72.4–94.6), is: Log odds of death amongst severe dengue cases = − 1.021 - 0.220(Serum bicarbonate) + 0.001(ALT) + 0.067(Age) - 0.190(Gender). Conclusion: This study showed that a large proportion of severe dengue occurred early, whilst patients were still febrile. The best prediction model to predict death at recognition of severe dengue is a model that incorporates serum bicarbonate and ALT levels.
Severe dengue; Mortality; Predict
Electronic supplementary material The online version of this article (10.1186/s12879-018-3141-6) contains supplementary material, which is available to authorized users.
Dengue infection has occurred in Malaysia for over a century [[
An epidemiological analysis in Malaysia revealed that all four DENV serotypes were found to be co-circulating during the period 2000–2012, although the predominant serotypes varied over time, both nationally and within the individual states in Malaysia [[
Along with this increase in burden of disease the management of dengue, especially severe dengue, continues to baffle physicians. Despite continuous efforts to upgrade and improve clinical practice guidelines, physicians are confronted with patients who do not fall neatly into typical description of the disease, hence pose a clinical conundrum. The spectrum of dengue infection ranges from asymptomatic to mild febrile illness, dengue with warning signs and severe dengue. Severe dengue is defined as end organ involvement, severe bleeding and/or severe plasma leakage. Although only a small percentage of patients went on to develop severe dengue, with a study quoting a proportion of 5% of all dengue infections studied [[
In that respect, additional knowledge would be indispensable in the management of dengue patients. Many studies have attempted to predict those that will develop severe dengue using clinical features and laboratory findings. There have also been studies that examined inflammatory markers, cytokines and genetic markers in predicting severity of dengue [[
The study was approved by the Medical Research and Ethics Committee (MREC), Ministry of Health of Malaysia (Research ID NMRR-15-2023-24,849). As the study involved data collection from case notes only, the MREC granted a waiver of informed consent. Our report is based on the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) 2015 guideline.
The study was a retrospective study of adult patients with severe dengue who were admitted into Hospital Kuala Lumpur throughout 2014. Sample recruitment was conducted from 1 January 2014 until 31 December 2014. Data were extracted from case notes of selected patients who fulfilled the study's inclusion criteria. A data collection pro forma was used to ensure integrity of data. As the information in the case notes were information used in patients' management, and thus would have been corrected for errors by managing clinicians, therefore we considered the information in the case notes as accurate.
Patients were selected for inclusion if, 1) they were ≥ 18 years old, 2) their presentation satisfied our criteria for suspected dengue, 3) the case fulfilled our definition of severe dengue, and 4) the presence of dengue viral infection was confirmed via NS1 antigen, high-titre level of IgG or positive IgM, from an admission serum sample. Our criteria for suspected dengue, based on the World Health Organization (WHO) 2009 criteria, were fever plus any two of: 1) aches and pain, 2) nausea and/or vomiting, 3) rash, 4) leucopenia, or 5) presence of any warning signs. The definition of severe dengue in our study was also based on WHO 2009 definition. We defined severe dengue by presence of any one of the following: 1) decompensated shock due to severe plasma leakage, 2) compensated shock due to severe plasma leakage, 3) respiratory compromise due to severe plasma leakage, 4) severe hepatitis, 5) severe bleeding that required intervention, or 6) severe organ involvement such as acute kidney injury defined by elevated serum creatinine (according to gender-specific levels), myocarditis or encephalopathy. Decompensated shock was defined by presence of systolic blood pressure (SBP) less than 90 mmHg, or mean arterial pressure (MAP) of less than 65 mmHg, or a drop in systolic blood pressure of more than 40 mmHg from patient's known usual baseline readings. Compensated shock required signs of impaired peripheral perfusion, occurring in combination rather than singly, in presence of systolic blood pressure of ≥90 mmHg. Severe hepatitis was defined as AST level ≥ 1000 IU/L, or ALT level ≥ 1000 IU/L.
Clinical management at Hospital Kuala Lumpur followed the Malaysian Clinical Practice Guideline on Management of Dengue Infection in Adults 2010 and the WHO 2009 clinical practice guideline for dengue.
Our outcome of interest was mortality among patients diagnosed with severe dengue. Patients were grouped into those who died and those who survived. We reviewed case notes to collect data. Data collected were baseline characteristics, clinical parameters, laboratory findings and time of events. Times of events were: dates and time of fever onset, time of admission, time of diagnosis of severe dengue, time of defervescence (start of temperature persistently < 38 °C) and time of occurrence of outcome. Clinical and laboratory parameters that were closest to the time of diagnosis of severe dengue were collected. These variables formed as candidate predictors for our prediction model. We also collected data on nadir and highest levels of relevant laboratory parameters, along with their timings, for descriptive purposes. Data on types of severe dengue diagnoses that occurred in a patient were also collected.
We calculated that a sample size of 13 deaths was needed for this study for an AUROC of 70%, at confidence level of 95% and power of 80%. Analyses were performed in SAS University Edition (Copyright © 2012–2016, SAS Institute Inc., Cary, NC, USA). Continuous variables were tested for uniformity using the Kolmogorov-Smirnov test and normality with the Shapiro-Wilk test. As our data were mostly non-parametric, we used non-parametric analyses for data interrogation. Categorical variables were expressed as frequencies and percentages. Continuous variables with non-normal distribution were summarised as median and inter-quartile range (IQR).
Identification of all factors that were significantly associated with mortality was made using the Chi-square test or Fisher's exact test for categorical variables and Mann–Whitney U test for continuous variables. We then performed multivariate analysis, with age and gender adjustments, to determine factors independently associated with mortality.
In order to build a predictive model, we used variables at the time of diagnosis of severe dengue as base for variable selection in model-building. Variable selection was performed using five-fold cross-validated Lasso regression. Selected variables were then used to build logistic regression models with cross-validation. Cross-validation and Lasso addressed overfitting that is known to occur in logistic regression. Logistic regression models built were composed of a combination of 2 of the Lasso-selected variables; and were adjusted for age and gender. All possible combinations were built. Finally, their AUROC were computed to allow comparison between models.
All tests of significance were 2-sided, and we took p-value < 0.05 indicating statistical significance.
A total of 199 adult patients diagnosed as severe dengue were admitted to the Department of Medicine, Kuala Lumpur Hospital between 1 January 2014 and 31 December 2015 and all were included. All cases had confirmation of dengue infection by NS1 antigen, high-titre IgG or IgM or combination. Of this, 20 patients died.
The clinical characteristics and laboratory parameters are shown in Table [
Table 1 Clinical characteristics and laboratory parameters of 199 patients hospitalised with severe dengue in 2014
All (N = 199) Died (N = 20) Survived (N = 179) p-value n n(%) or median(IQR) n n(%) or median(IQR) n n(%) or median(IQR) Age, years 197 30.8 (24.7–41.3) 20 43.5 (30.3–54.8) 177 30.2 (23.9–38.9) 0.0003 BMI 117 24.9 (21.3–29.7) 6 23.6 (22.7–24.1) 111 25.3 (21.3–30.0) 0.29 Gender, Male 199 127 (63.8%) 20 13 (65%) 179 114 (63.7%) 0.91 Presence of any co-morbidity 198 62 (31.2%) 20 9 (45%) 178 53 (29.6%) 0.16 Co-morbidity COPD/BA 9 (4.6%) 0 (0%) 9 (5.1%) DM 24 (12.1%) 5 (25%) 19 (10.7%) Hypertension 9 (4.6%) 2 (10%) 7 (3.9%) IHD 2 (1%) 1 (5%) 1 (0.6%) Pregnancy 8 (4%) 1 (5%) 7 (3.9%) Others 10 (5.1%) 0 (0%) 10 (5.6%) Multiple co-morbidities 198 20 (10.1%) 20 5 (25%) 178 15 (8.4%) 0.04a NS1 198 132 (66.7%) 20 13 (65%) 178 119 (66.9%) 0.87 High-titre IgG 198 47 (23.7%) 20 5 (25%) 178 42 (23.6%) 0.89 IgM 198 115 (58.1%) 20 14 (70%) 178 101 (56.7%) 0.25 Persistent vomiting 198 114 (57.6%) 20 14 (70%) 178 100 (56.2%) 0.24 Abdominal pain 198 89 (45%) 20 7 (35%) 178 82 (46.1%) 0.35 Lethargy 198 96 (48.5%) 20 15 (75%) 178 81 (45.5%) 0.01 Palpable liver 198 16 (8.1%) 20 2 (10%) 178 14 (7.9%) 0.67a Fluid accumulation 198 44 (22.2%) 20 4 (20%) 178 40 (22.5%) 1.00a Bleed 198 49 (24.8%) 20 13 (65%) 178 36 (20.2%) < 0.0001a Hct > 46 198 77 (38.9%) 20 10 (50%) 178 67 (37.6%) 0.28 Low Plt < 46 198 106 (53.5%) 20 15 (75%) 178 91 (51.1%) 0.04 Albumin < 34 198 106 (53.5%) 20 13 (65%) 178 93 (52.3%) 0.28 Number of warning signs 199 20 179 NSb 0 9 (4.5%) 0 (0%) 9 (5.03%) 1 17 (8.5%) 0 (0%) 17 (9.5%) 2 35 (17.6%) 2 (10%) 33 (18.4%) 3 35 (17.6%) 4 (20%) 31 (17.3%) 4 49 (24.6%) 6 (30%) 43 (24.0%) 5 29 (14.6%) 2 (10%) 27 (15.1%) 6 16 (8%) 3 (15%) 13 (7.3%) 7 5 (2.5%) 0 (0%) 5 (2.8%) 8 3 (1.5%) 2 (10%) 1 (0.6%) 9 1 (0.5%) 1 (5%) 0 (0%) Presence of any warning signs 198 190 (95.5%) 20 20 (100%) 178 170 (95.0%) 1.00a Severe dengue by type Decompensated shock 58 (29.1%) 10 (50%) 48 (26.8%) Compensated shock 69 (34.7%) 4 (20%) 65 (36.3%) Respiratory compromise 42 (21.1%) 2 (10%) 40 (22.3%) Severe bleeding 24 (12.1%) 10 (50%) 14 (7.8%) Severe hepatitis 40 (20.1%) 8 (40%) 32 (17.9%) AKI 35 (17.6%) 8 (40%) 27 (15.1%) Encephalitis 6 (3.0%) 2 (10%) 4 (2.2%) Other 5 (2.5%) 1 (5%) 4 (2.2%) Number of types of severe dengue NSb 1 139 (69.8%) 12 (60%) 127 (70.9%) 2 53 (26.6%) 4 (20%) 49 (27.4%) 3 5 (2.5%) 3 (15%) 2 (1.1%) 4 2 (1.0%) 1 (5%) 1 (0.6%) Blood products given 198 57 (28.8%) 20 18 (90%) 178 39 (21.9%) < 0.0001 SBP, mmHg 198 103 (90–120) 20 98 (87–133) 178 103 (90–118) 0.88 DBP, mmHg 198 63 (53–74) 20 60.5 (48–84) 178 63 (54–74) 0.92 MAP, mmHg 198 77 (65–89) 20 74.3 (61–102) 178 77.3 (66–88) 0.96 PR, beats per minute 197 98 (86–110) 20 110 (102–120) 177 96 (86–109) 0.01 Shock Index, bpm/mmHg 197 1.2 (1.0–1.5) 20 1.3 (1.0–1.9) 177 1.2 (1.0–1.4) 0.25 RR, breaths/min 182 20 (18–24) 20 22 (19–30) 162 20(18–23) 0.07 WBC, ×103/μL 198 4.2 (2.9–5.7) 20 5.3 (2.7–8.4) 178 4.1 (2.9–5.6) 0.15 Hb, g/dL 198 14.2(12.3–16.2) 20 13.9 (11.7–17.1) 178 14.2 (12.3–16.1) 0.97 Hct, % 198 42 (37–47.0) 20 42.5 (31.0–49.5) 178 42 (37.0–47.0) 0.91 Platelet, × 103/μL 197 43 (16–97) 20 26 (8–41) 177 53 (18–105) 0.005 Serum creatinine, μmol/L 195 79 (63–101) 20 110 (67–333) 175 75 (63–97) 0.01 AST, U/L 184 165 (66–600) 19 1175 (123–1920) 165 152 (63–456) 0.01 ALT, U/L 192 104 (34–257) 20 365 (62–755) 172 95 (31–211) 0.01 Troponin T, ng/mL 17 0.02 (0.009–0.3) 6 0.4 (0.05–1.9) 11 0.009 (0.007–0.03) 0.10 Serum bicarbonate, mmol/L 192 21.1 (18.5–23.1) 20 16.2 (11.2–18.8) 172 21.5 (19.4–23.2) < 0.0001 Serum lactate, mmol/L 149 1.6 (1.1–2.3) 19 3.2 (2.1–6.5) 130 1.5 (1.1–2.1) < 0.0001 Nadir WBC, × 103/μL 198 2.7 (1.94–3.8) 20 2.6 (2.1–4.1) 178 2.7 (1.9–3.7) 0.47 Highest Hct, % 198 45.1 (40.9–49.0) 20 46.9 (41.0–50.2) 178 45.0 (40.9–49.0) 0.38 Nadir Platelet, × 103/μL 198 18 (7–44) 20 7 (2–21) 178 19 (8–45) 0.005 Highest serum creatinine, μmol/L 198 87 (69–112) 20 249 (175–378) 178 83 (68–102) < 0.0001 Highest AST, U/L 193 262 (100–1024) 20 4763 (658–21,547) 173 244 (98–589) < 0.0001 Highest ALT, U/L 197 147 (58–450) 20 1243 (243–3780) 177 117 (55–303) < 0.0001 Highest Troponin T, ng/mL 22 0.01 (0.005–0.3) 5 0.7 (0.1–1.9) 17 0.01 (0.005–0.03) 0.07 Lowest Serum bicarbonate, mmol/L 195 19.5 (16.9–21.4) 20 8.5 (6.7–13.1) 175 19.9 (17.9–21.5) < 0.0001 Highest Serum lactate, mmol/L 189 1.9 (1.3–2.8) 20 16.0 (6.7–18.5) 169 1.8 (1.2–2.3) < 0.0001 Total fluids given, mL 193 6034 (3470–8707) 20 9808 (7184–16,692) 173 5610 (3307–8259) 0.0004
n count of non-missing observations;
Systolic blood pressure < 90 mmHg was present in 22.2% of patients and there were 16.2% of patients with SBP < 90 mmHg who were still febrile (temperature > 38 °C). Mean arterial pressure < 65 mmHg was present in 22.7%, diastolic blood pressure < 50 mmHg in 18.7% and tachycardia defined by pulse rate > 100 beats/min in 47.7%. The proportion of patients diagnosed with shock who had temperature > 38 °C were 38.9%. Respiratory rate ≥ 25 breaths per minute occurred in 18.1% of patients.
Leucopenia was present only in 46% of patients and platelet count < 20 × 10
Haematocrit was elevated in 41.3% of males (Hct > 46%) and 43.1% of females (Hct > 40%). Serum creatinine was elevated in 27.2% of males (serum creatinine > 106 μmol/L) and in 11.4% of females (serum creatinine > 96 μmol/L).
Timing of clinical events and phases of clinical course are shown in Table [
Table 2 Timing of clinical events in 199 patients hospitalised with severe dengue in 2014
All (N = 199) Died (N = 20) Survived (N = 179) p-value n n (%) or median (IQR) n n (%) or median (IQR) n n (%) or median (IQR) 1Day of admission 199 4.00 (2.81–5.00) 20 3.51 (2.58–4.38) 179 4.03 (2.93–5.00) 0.29 2Day of development of severe dengue 198 4.63 (3.56–5.67) 20 4.07 (3.22–5.81) 178 4.65 (3.66–5.67) 0.39 3Day of defervescence 194 4.96 (3.92–6.04) 18 4.08 (3.00–5.13) 176 5.02 (4.03–6.13) 0.05 4Day of outcome 199 8.75 (7.16–11.0) 20 6.81 (4.91–12.82) 179 8.80 (7.46–10.67) 0.08 Phase at admission, febrile 194 153 (78.8) 18 10 (55.6) 176 143 (81.3) 0.03a Phase at severe dengue diagnosis, febrile 193 114 (59.1) 18 6 (33.3) 175 108 (61.7) 0.02 Severe dengue upon presentation 198 73 (36.9) 20 8 (40.0) 178 65 (36.5) 0.76 5Day of nadir WBC 199 4.63 (3.75–5.71) 20 3.74 (2.93–5.34) 179 4.71 (3.88–5.73) 0.07 6Day of nadir platelet 199 5.50 (4.64–6.50) 20 4.79 (3.40–6.96) 179 5.54 (4.79–6.50) 0.11 7Day of lowest serum bicarbonate 197 5.00 (3.92–6.04) 20 5.36 (3.56–8.94) 177 4.92 (3.96–5.92) 0.15 8Day of highest serum lactate 190 5.38 (4.13–6.60) 20 5.44 (3.93–9.70) 170 5.35 (4.13–6.50) 0.32 9Day of highest serum ALT 198 5.56 (4.42–6.75) 20 5.41 (4.05–7.02) 178 5.58 (4.42–6.75) 0.88 10Day of highest serum AST 194 5.35 (4.29–6.54) 20 5.26 (4.18–7.39) 174 5.35 (4.38–6.50) 0.81 11Day of highest serum creatinine 199 4.67 (3.67–5.79) 20 4.81 (4.00–8.38) 179 4.67 (3.58–5.71) 0.10
n count of non-missing observations;
We also found that nadir platelet occurred almost a day later after nadir WBC in both groups. However, these timings were not statistically significantly different between groups. Similarly, lowest serum bicarbonate occurred before serum lactate peaked however no statistical difference between groups in terms of timings were found.
Univariate analyses found 22 clinical and laboratory parameters which were statistically significantly different between patients who died compared to those who survived (Tables [
Our univariate analyses also showed that mortality was also associated with lower nadir platelet level. In the group that died, highest median serum creatinine was 3 times higher than survivors, highest median AST was almost 20 times higher, highest median ALT was more than 10 times higher, highest median lactate was more than 8 times higher and lowest median serum bicarbonate was more than 2-fold lower. These had statistically significant difference between groups. Non-survivors were found to have received statistically significantly higher volume of fluid (1.7 times more) and a larger proportion (4.1 times) who received blood products transfusion.
Multivariate analysis, with adjustment for age and gender, revealed lethargy, bleeding, pulse rate, serum bicarbonate and serum lactate, to be statistically significant as independent predictors of death among severe dengue cases (Table [
Table 3 Multivariate analysis of potential factors that predict mortality at the time of diagnosis of severe dengue
Parameters Adjusted OR 95% CI p Lethargy 3.84 (1.23–12.03) 0.0208 Bleed 8.88 (2.91–27.15) 0.0001 Multiple co-morbidities 1.28 (0.32–5.15) 0.7258 Pulse rate 1.04 (1.01–1.07) 0.0058 Serum bicarbonate 0.79 (0.70–0.89) < 0.0001 Serum lactate 1.27 (1.09–1.47) 0.0019 Plt 0.981 (0.97–1.00) 0.0231 ALT 1.001 (1.00–1.002) 0.0046 AST 1.001 (1.00–1.001) 0.0152 Serum creatinine 1.008 (1.00–1.01) 0.0044
OR odds ratio, CI confidence interval
Lasso selected age, pulse rate, bleeding, serum bicarbonate, serum lactate, serum creatinine, AST and ALT out of 29 candidate variables. Pulse rate, bleeding, serum bicarbonate, serum lactate, serum creatinine, AST and ALT were then used to build models composed of a combination of a pair of these variables, with age and gender adjustments. Twenty-one models were built (Additional file 1: Table S1). We found that serum bicarbonate-based models outperformed lactate-based models (Table [
Table 4 Area under curve of receiver operating curve (AUROC) of models for predicting mortality among cases of severe dengue
Model AUROC (95% CI) Serum bicarbonate - ALT 83.5 (72.4–94.6) Serum bicarbonate - Pulse rate 83.4 (73.5–93.4) Serum bicarbonate - Serum creatinine 83.1 (72.0–94.3) Serum bicarbonate - Bleed 82.9 (70.5–95.2) Serum lactate - ALT 80.8 (67.2–94.4) Serum lactate - Pulse rate 78.7 (65.7–91.8) Serum lactate - Serum creatinine 77.7 (63.9–91.5) Serum lactate - Bleed 81.1 (68.2–94.0)
AUROC area under curve; CI confidence interval
Our study made two notable findings: the first being description of aspects of severity of a severe dengue cohort; and the second, the rigorous building of a predictive model, which was selected by assessment and comparison of many models, that predicts death early at the recognition of severe dengue.
Our cohort bears some characteristics of Malaysia's population. The National Health Morbidity Survey 2015 determined that 30.6% of the population were obese and 17.5% were diabetics, figures similar to this study [[
The fatality rate of this study was 10.1%. Previous studies similar to ours had rates of 3.8% [[
Our study showed that about a third of patients presented as severe dengue upon admission to hospital whilst the remaining developed severe dengue after admission. Duration of fever onset to severe dengue diagnosis were shorter in those who presented as severe dengue as compared to those who developed severe dengue after admission, median(IQR) 4.38 [[
Guidelines in dengue have repeatedly highlighted that the timing of deterioration is around the time of defervescence and within the critical phase that ensues. It has also been noted that organ impairment follows the same timing. Intriguingly, we found that almost 60% of patients were still febrile at diagnosis of severe dengue; and the proportion of patients who were still febrile at diagnosis of severe dengue was statistically significantly more in those who survived as compared to those who died (Table [
Our findings that nadir platelet occurred about a day after nadir WBC is consistent with a previous study [[
Based on our cohort, independent predictors of death at the time when the diagnosis of severe dengue was made were: lethargy, bleeding, pulse rate, serum bicarbonate and serum lactate. There have been only 2 studies so far, that examined factors associated with death among severe dengue patients, which used WHO 2009 classification. A study utilising notification database from Brazil [[
We found that the best prediction model to predict death at the time when the diagnosis of severe dengue was made is a model that incorporated serum bicarbonate and ALT levels taken at that time. This is rather fortuitous for a few reasons. Firstly, both serum bicarbonate and ALT are objective measures as opposed to subjective warning signs such as lethargy and bleeding which have inherent variability in establishing their presence and severity. Secondly, both are currently accessible laboratory tests.
A recent study on serum lactate in dengue [[
Unexpectedly, serum creatinine was not found to be an independent predictor of death by multivariate analysis. However, its incorporation into our models led to good AUROC performances.
Under the assumption that the time of recognition of severe dengue is approximately the actual time of development of severe dengue, we believe that prognosticating mortality in patients with severe dengue at the time of its recognition provides a sensible approach. We postulate that at this time, underlying pathophysiological processes which determine outcome would most likely have reach significance. Prognosticating prematurely before this moment may have little specificity, as the ultimate outcome determining processes may yet to occur. Prognosticating too late however is obviously futile. With our model management decisions may be better informed in terms of resource allocation, especially in conditions of high volume care. It has to be noted however that the underlying outcome determining pathophysiology has yet to be clearly elucidated. Further investigation into the kinetics of biochemistry with respect to timing of events in dengue, in particular the time of development of severe dengue, is needed and may perhaps fill this gap.
The main limitation of our study was the retrospective design. However, data accuracy was reasonable as management of patients followed standard management guidelines for dengue which have clear specifications of timing of blood investigations. Though only a single centre study, we believe the sample size was adequate as illustrated by the results of our findings. Finally, though we have rigorously built a predictive model and took steps to address overfitting, the actual performance of any model will vary according to the population it is applied on. As we have mentioned, our cohort is different from similar studies, hence our model will require population specific external validation and assessment.
In conclusion, the degree of severity observed in this study was more serious than those reported in similar studies. We showed that a large proportion of severe dengue occurred early, whilst patients were still febrile. Finally, the regression equation to our model is: Log odds of death among severe dengue cases = − 1.021 - 0.220(Serum bicarbonate) + 0.001(ALT) + 0.067(Age) - 0.190(Gender).
Concept and design: Md-Sani, Md-Noor, Han, Abd-Rahman. Acquisition, analysis, or interpretation of data: Md-Sani, Han, Gan, Rani, Tan, Rathakrishnan, A-Shariffuddin. Drafting of the manuscript: Md-Sani, Md-Noor, Han. Critical revision of the manuscript for important intellectual content: Md-Sani, Md-Noor, Abd-Rahman, Han. Administrative, technical, or material support: Md-Sani, Md-Noor, A-Shariffuddin. All authors read and approved the final manuscript.
The study was approved by the Medical Research and Ethics Committee (MREC), Ministry of Health of Malaysia (Research ID NMRR-15-2023-24,849). As the study involved data collection from case notes only, the MREC granted a waiver of informed consent.
Not applicable as the study involved data collection from case notes only, the MREC (Medical Research and Ethics Committee) granted a waiver of informed consent (Research ID NMRR-15-2023-24,849).
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The authors would like to thank all the staff and nurses of Dengue Wards, Department of Medicine, Kuala Lumpur Hospital and the Records Office, Kuala Lumpur Hospital for their assistance in the handling of medical records. We also would like to thank the Director General of Health Malaysia for his permission to publish this article.
Data are available upon direct request to the corresponding author, Dr. Julina MD-NOOR, julinamn@gmail.com, subject to approval by the Director-General of Health, Ministry of Health, Malaysia.
By Saiful Safuan Md-Sani; Julina Md-Noor; Winn-Hui Han; Syang-Pyang Gan; Nor-Salina Rani; Hui-Loo Tan; Kanimoli Rathakrishnan; Mohd Azizuddin A-Shariffuddin and Marzilawati Abd-Rahman