Start Submission Become a Reviewer

Reading: Cardiometabolic Morbidity and Other Prognostic Factors for Mortality in Adult Hospitalized C...

Download

A- A+
Alt. Display

Original Research

Cardiometabolic Morbidity and Other Prognostic Factors for Mortality in Adult Hospitalized COVID-19 Patients in North Jakarta, Indonesia

Authors:

Arvin Pramudita ,

Department of Cardiology and Vascular Medicine, Faculty of Medicine Universitas Indonesia, National Cardiovascular Center Harapan Kita, Jakarta; Koja General Hospital, Jakarta, ID
X close

Siti Rosidah,

Koja General Hospital, Jakarta, ID
X close

Novi Yudia,

Koja General Hospital, Jakarta, ID
X close

Jeffri Simatupang,

Koja General Hospital, Jakarta, ID
X close

Wulan Pingkan Sigit,

Koja General Hospital, Jakarta, ID
X close

Rita Novariani,

Koja General Hospital, Jakarta, ID
X close

Priscilia Myriarda,

Koja General Hospital, Jakarta, ID
X close

Bambang Budi Siswanto

Department of Cardiology and Vascular Medicine, Faculty of Medicine Universitas Indonesia, National Cardiovascular Center Harapan Kita, Jakarta, ID
X close

Abstract

Background: Although there have been several studies investigating prognostic factors for mortality in COVID-19, there have been lack of studies in low- and middle-income countries, including Indonesia. To date, the country has the highest mortality rate among Asian countries.

Objective: We sought to identify the prognostic factors of mortality in hospitalized patients with COVID-19 in Jakarta.

Methods: In this retrospective cohort study, we included all adult inpatients (≥18 years old) with confirmed COVID-19 from Koja General Hospital (North Jakarta, Indonesia) who had been hospitalized between March 20th and July 31st, 2020. Demographic, clinical, laboratory, and radiology data were extracted from the medical records and compared between survivors and non-survivors. Univariate and multivariate logistic regression analysis were used to explore the prognostic factors associated with in-hospital death.

Results: Two hundred forty-three patients were included in the study, of whom 32 died. Comorbid of hypertension (OR 3.59; 95% CI 1.12–11.48; p = 0.031), obesity (OR 6.34; 95% CI 1.68–23.98; p = 0.007), immediate need of HFNC and/or IMV (OR 64.93; 95% CI 11.08–380.61; p < 0.001), abnormal RDW (OR 3.68; 95% CI 1.09–12.34; p = 0.035), ALC < 1,000/µL (OR 3.51; 95% CI 1.08–11.44; p = 0.038), D-dimer > 500 ng/mL (OR 9.36; 95% CI 1.53–57.12; p = 0.015) on admission, as well as chloroquine treatment (OR 3.61; 95% CI 1.09–11.99; p = 0.036) were associated with greater risk of overall mortality in COVID-19 patients. The likelihood of mortality increased with increasing number of prognostic factors.

Conclusion: The potential prognostic factors of hypertension, obesity, immediate need of HFNC and/or IMV, abnormal RDW, ALC < 1,000/µL, D-dimer > 500 ng/mL, and chloroquine treatment could help clinicians to identify COVID-19 patients with poor prognosis at an early stage.
How to Cite: Pramudita A, Rosidah S, Yudia N, Simatupang J, Sigit WP, Novariani R, et al.. Cardiometabolic Morbidity and Other Prognostic Factors for Mortality in Adult Hospitalized COVID-19 Patients in North Jakarta, Indonesia. Global Heart. 2022;17(1):9. DOI: http://doi.org/10.5334/gh.1019
276
Views
101
Downloads
1
Twitter
  Published on 18 Feb 2022
 Accepted on 24 Jan 2022            Submitted on 28 Feb 2021

Introduction

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in December 2019 in Wuhan, China. The first confirmed case of coronavirus disease 2019 (COVID-19) in Indonesia was reported on March 2, 2020. Soon after, the disease has spread rapidly within the country. By the end of 2020, approximately 735,000 cases had been diagnosed with 22,138 deaths in Indonesia [1].

Data from Chinese Center for Disease Control and Prevention (CCDC), including more than 72,000 people with COVID-19 from the country, showed that 81% were mild (absent or mild pneumonia), 14% were severe (hypoxia, dyspnea, >50% lung involvement within 24–48 hours), 5% were critical (shock, respiratory failure, multiorgan dysfunction), and 2.3% were fatal [2]. A number of factors associated with mortality have been identified from China, such as older age, male sex, presence of comorbidities, and abnormal lab findings (high WBC, high LDH, high procalcitonin, high D-dimer, low albumin level) [2, 3, 4, 5, 6].

To date, Indonesia has the highest mortality rate among Asian countries with third most confirmed cases of COVID-19 after India and Iran [1]. As part of low- and middle-income countries (LMICs), this situation represents a big challenge for Indonesia with its constrained critical care capacity to treat COVID-19 and limited financial resources [7]. Despite of that, little is known about the prognostic factors contributing to the high mortality rate in Indonesia. Understanding these factors is crucial, not only for early detection of high-risk patient in the country’s hospital setting, but also to guide local authorities developing appropriate policies to avoid the collapse of the healthcare system. Hence, this study was performed to identify the prognostic factors of mortality in hospitalized patients with COVID-19 in Jakarta, the capital city of Indonesia.

Methods

Study design

This retrospective cohort study was conducted in Koja General Hospital, a tertiary and one of COVID-19 referral hospital in Jakarta, Indonesia. All consecutive adult patients (age ≥ 18 years) diagnosed with COVID-19 and hospitalized between March 20th and July 31st, 2020 were enrolled. A confirmed case of COVID-19 was defined as a positive result on real-time reverse transcriptase polymerase chain reaction (RT-PCR) for the presence of SARS-CoV-2 in nasopharyngeal swab specimens [8]. Patients who were referred to other hospital or still on treatment were excluded.

Data collection

Demographic, clinical, laboratory, and radiology data were extracted from medical records of the participants. Demographic and clinical data included age, sex, symptoms on admission (cephalgia, fever, cough, dyspnea, dysphagia, rhinorrhea, chest pain, nausea, diarrhea, dyspepsia), comorbidities (hypertension, cardiovascular disease, diabetes mellitus, chronic kidney disease, asthma, tuberculosis, and obesity), vital signs on admission (blood pressure and heart rate), immediate need of supplemental oxygen, and treatment in the hospital (antibiotics, oseltamivir, and chloroquine). Laboratory data consisted of complete blood count, blood biochemistry, C-reactive protein (CRP) and D-dimer taken on the first day of admission. Chest X-ray was also taken on the first day and interpreted by a radiologist, seeking for cardiomegaly as the only indicator that could be measured objectively. The end-point of our study was overall in-hospital mortality rate, regardless of length of stay or cause of death.

Statistical analysis

For the statistical analysis, age was categorized into (1) <45 years old and (2) ≥45 years old, following previous similar study in Indonesia [9]. Symptoms on admission, comorbidities, vital signs on admission, cardiomegaly, and treatment options in the hospital were recorded as (1) yes or (2) no. Obesity was defined as body mass index (BMI) ≥25 kg/m2 based on the guideline from World Health Organization for Asia-Pacific population [10]. Blood pressure (BP) on admission was categorized using the cut-off of 140/90 mmHg [11]. Immediate need of supplemental oxygen was based on clinical judgement of the physician to maintain patients’ normal peripheral oxygen saturation and was recorded as (1) immediate need of high flow nasal cannula (HFNC) and/or invasive mechanical ventilation (IMV), (2) immediate need of nasal cannula up to non-rebreather mask (NRM), or (3) no immediate need of oxygen. Statistically significant hematological values were categorized as (1) normal and (2) abnormal based on reference values used at our hospital. Categories for neutrophil-to-lymphocyte ratio (NLR) were (1) >3.13 and (2) ≤3.13 [12]. Categories for absolute lymphocyte count (ALC) were (1) <1,000/µL and (2) ≥1,000/µL [13]. Categories for CRP were (1) >10 mg/dL and (2) ≤10 mg/dL [14]. Categories for D-dimer were (1) >500 mg/dL and (2) ≤500 mg/dL [14].

Continuous variables are expressed as means ± standard deviation or median [range] and were compared by Student’s t-test or the Mann-Whitney U test. Categorical variables are described as number (%) and were compared by the χ2 test or Fisher’s exact test. Univariate logistic regression analysis was performed to identify prognostic factors of mortality. Multivariate logistic regression analysis was conducted with variables that showed p < 0.05 in univariate analysis. In all analyses, two-tailed p < 0.05 was taken to indicate statistical significance. All statistical analyses were performed using SPSS software (ver. 25.0; IBM, North Castle, NY, USA).

Ethical statement

This study was reviewed and approved by the Ethical Committee of Koja General Hospital (04/KOMEP/2020). The requirement for informed consent was waived because of the retrospective study design. The final follow-up date was July 31st, 2020.

Results

There were 253 hospitalized patients with confirmed COVID-19 throughout our time period. Five cases were excluded as 2 patients were transferred to other hospital by patients’ preference and 3 still hospitalized as of July 31st, 2020. A total of 248 patients were enrolled, of whom 243 cases were included in the study. Five cases were excluded due to incomplete key information in their medical records (Figure 1).

Flowchart
Figure 1 

Flowchart.

COVID-19 = coronavirus disease 2019.

Baseline characteristics of all patients are summarized in Table 1. The patients in the death group were significantly older than the patients in the alive group (54.2 ± 14 vs. 47.1 ± 14.3, respectively, p = 0.009). There was no difference in sex between both groups. The death group was significantly more likely to have comorbidities of hypertension (53.1% vs. 29.4%, respectively, p = 0.008), diabetes mellitus (43.8% vs. 17.1%, respectively, p = 0.001) and obesity (37.5% vs. 14.2%, respectively, p = 0.001). On admission, symptom of dyspnea (56.3% vs. 35.5%, respectively, p = 0.025) and radiologic findings of cardiomegaly (40.6% vs. 20.4%, respectively, p = 0.011) were significantly more common in the death group than the alive group. More patients in the death group also needed immediate oxygen supplementation than those in the alive group significantly (p < 0.001). Chloroquine was given more frequently in the death group significantly (65.6% vs. 45%, respectively, p = 0.030).

Table 1

Baseline characteristics of the study participants with COVID-19.


CHARACTERISTICS DEATH (N = 32) ALIVE (N = 211) P VALUE

Age

    Mean ± SD (years) 54.2 ± 14 47.1 ± 14.3 0.009

    Age ≥ 45 years (%) 81.3 57.3 0.010

Sex, male (%) 53.1 53.1 0.996

Symptoms on admission

    Cephalgia (%) 15.6 23.7 0.309

    Fever (%) 56.3 46 0.278

    Cough (%) 71.9 55 0.072

    Dyspnea (%) 56.3 35.5 0.025

    Dysphagia (%) 9.4 13.7 0.496

    Rhinorrhea (%) 3.1 9.5 0.233

    Chest pain (%) 6.3 3.8 0.514

    Nausea (%) 21.9 31.8 0.258

    Diarrhea (%) 0 6.6 0.133

    Dyspepsia (%) 25 16.1 0.215

Comorbidities

    Hypertension (%) 53.1 29.4 0.008

    Cardiovascular disease (%) 25 12.8 0.067

    Diabetes mellitus (%) 43.8 17.1 0.001

    Chronic kidney disease (%) 9.4 3.8 0.164

    Asthma (%) 0 2.8 0.425

    Tuberculosis (%) 9.4 5.2 0.275

    Obesity (%) 37.5 14.2 0.001

Vital signs on admission

    BP ≥ 140/90 mmHg (%) 34.4 40 0.544

    Heart rates > 100 beats/min (%) 43.8 31.3 0.162

Immediate need of supplemental oxygen <0.001

    HFNC and/or IMV (%) 65.6 6.2

    Nasal cannula up to NRM (%) 28.1 29.4

    Not needed (%) 6.3 64.5

Radiologic findings

    Cardiomegaly (%) 40.6 20.4 0.011

Treatment in hospital

    Antibiotics (%) 96.9 88.2 0.137

    Oseltamivir (%) 93.8 88.6 0.382

    Chloroquine (%) 65.6 45 0.030

COVID-19 = coronavirus disease 2019, SD = standard deviation, BP = blood pressure, HFNC = high-flow nasal cannula, IMV = invasive mechanical ventilation, NRM = non-rebreather mask.

Laboratory findings on hospital admission are summarized in Table 2. In complete blood counts, white blood cells (WBC) count (9,700 [4,460 – 28,860] vs. 8,540 [2,790 – 35,450], respectively, p = 0.049 – 0.024) and neutrophil-to-lymphocyte ratio (NLR) (8.66 [1.91 – 30.50] vs. 3.37 [0.31 – 47.15], respectively, p < 0.001) were higher in the death group than the alive group. Absolute lymphocyte count (ALC) (1,008 [242 – 7,821] vs. 1,588 [16 – 6,219], respectively, p < 0.001) and platelet count (230,781 ± 92,319 vs. 275,110 ± 100,158, respectively, p = 0.019) were significantly lower in the death group. There was also a higher percentage of abnormal red cell distribution width (RDW) in the death group (43.8% vs. 22.9%, respectively, p = 0.021).

Table 2

Laboratory findings on admission in patients with COVID-19.


VARIABLES DEATH (N = 32) ALIVE (N = 211) P VALUE

Hemoglobin (g/dL, median [range]) 12.5 [7.6–16.9] 13.2 [5.7–18.3] 0.118–0.059

White blood cells (/µL)

    Median [range] 9700 [4460–28860] 8540 [2790–35450] 0.049–0.024

    Distribution – abnormal WBC (%) 40.6 22.7 0.030

Hematocrite (%, median [range]) 36.1 [20.9–49.7] 37.9 [17–52.1] 0.145–0.072

Platelet (/µL)

    Mean ± SD 230781 ± 92319 275110 ± 100158 0.019

    Distribution – abnormal PLT (%) 21.9 13.3 0.153

Red cell distribution width (%)

    Median [range] 13.8 [11.7–19.6] 13.1 [11.3–38] 0.006–0.003

    Distribution – abnormal RDW (%) 43.8 22.9 0.012

Neutrophil-lymphocyte ratio

    Median [range] 8.66 [1.91–30.50] 3.37 [0.31–47.15] <0.001

    Distribution – NLR > 3.13 (%) 84.4 54 0.001

Absolute lymphocyte count (/µL)

    Median [range] 1008 [242–7821] 1588 [16–6219] <0.001

    Distribution – ALC < 1,000 (%) 50 18.5 <0.001

Random blood glucose (mg/dL)

    Median [range] 142 [63–550] 114 [64–522] <0.001

    Distribution – RBG ≥ 200 (%) 28.1 12.3 0.018

Blood urea nitrogen
(mg/dL, median [range])
51.8 [13–362.6] 21.4 [3.4–184] <0.001

Creatinine (mg/dL)

    Median [range] 1.46 [0.59–16.12] 0.87 [0.36–105] <0.001

    Distribution – Cr > 1.2 (%) 62.5 22.7 <0.001

C-reactive protein (mg/dL)

    Median [range] 11.2 [0.38–31.29] 1.90 [0–32] <0.001

    Distribution – CRP > 10 (%) 62.5 26.5 <0.001

D-dimer (ng/mL)

    Median [range] 2922 [590–10000] 987 [141–15441] <0.001

    Distribution – D-dimer > 500 (%) 93.8 46.9 <0.001

n = 21 n = 148

Alanine transaminase
(U/L, median [range])
38 [10–480] 28.5 [4–247] 0.06–0.03

Aspartate transaminase (U/L, median [range]) 55 [15–1500] 26 [12–269] <0.001

n = 32 n = 149

Sodium (mEq/L, median [range]) 134 [119–159] 138 [109–152] 0.005–0.002

Potassium (mEq/L, median [range]) 4.16 [2.8–8] 3.63 [1.62 -4.93] <0.001

Chloride (mEq/L, median [range]) 104 [88–115] 105 [90–114] 0.823–0.411

COVID-19 = coronavirus disease 2019, abnormal WBC = WBC < 4000 or > 11000 (/µL), abnormal PLT = PLT < 150000 or > 450000 (/µL), abnormal RDW = RDW > 14%.

With regard to blood chemistry, creatinine level was significantly higher in the death group than the alive group (1.46 [0.59 – 16.12] vs. 0.87 [0.36 – 105] mg/dL, respectively, p < 0.001)). Concentrations of blood urea nitrogen and random blood glucose were also significantly higher in the death group. Not all of the patients were assessed for their liver function and electrolyte status. From the available data (n = 169), concentrations of alanine transaminase and aspartate transaminase were significantly higher in the death group. On the other hand (n = 181), sodium level was significantly lower yet potassium level was significantly higher in the death group.

C-reactive protein, as an inflammation-related marker, was significantly higher in the death group than the alive group (11.2 [0.38 – 31.29] vs. 1.90 [0 – 32] mg/dL, respectively, p < 0.001). The same result was also applied for D-dimer (2,922 [590 – 10,000] vs. 987 [141 – 15,441] ng/mL, respectively, p < 0.001).

Prognostic factors for mortality of COVID-19

Multivariate analysis with logistic regression model was performed using selected factors from univariate analysis (Table 3) and demonstrated that comorbid of hypertension (odds ratio [OR] 3.59; 95% confidence interval (CI) 1.12–11.48; p = 0.031), obesity (OR 6.34; 95% CI 1.68–23.98; p = 0.007), immediate need of HFNC and/or IMV (OR 64.93; 95% CI 11.08–380.61; p < 0.001), abnormal RDW (OR 3.68; 95% CI 1.09–12.34; p = 0.035), ALC < 1,000/µL (OR 3.51; 95% CI 1.08–11.44; p = 0.038), D-dimer > 500 ng/mL (OR 9.36; 95% CI 1.53–57.12; p = 0.015) on admission, as well as chloroquine treatment (OR 3.61; 95% CI 1.09–11.99; p = 0.036) were associated with greater risk of overall mortality in COVID-19 patients (Table 4). The likelihood of mortality increased with increasing number of prognostic factors (p < 0.001, test for trend) (Figure 2).

Table 3

Univariate analysis of prognostic factors for overall mortality in COVID-19.


PROGNOSTIC FACTORS OR (95% CI) P VALUE

Age

    ≥45 years 3.22 (1.27–8.16) 0.010

    <45 years Reference

Dyspnea

    Yes 2.33 (1.10–4.95) 0.025

    No Reference

Hypertension

    Yes 2.72 (1.28–5.79) 0.008

    No Reference

Diabetes mellitus

    Yes 3.78 (1.72–8.29) 0.001

    No Reference

Obesity

    Yes 3.62 (1.60–8.16) 0.001

    No Reference

Immediate need of supplemental oxygen

    HFNC and/or IMV 109.85 (23.13–521.70) <0.001

    Nasal cannula up to NRM 9.87 (2.07–47.04) 0.004

    Not needed Reference

Cardiomegaly

    Yes 2.67 (1.22–5.84) 0.011

    No Reference

Abnormal WBC

    Yes 2.32 (1.07–5.05) 0.030

    No Reference

Abnormal RDW

    Yes 2.62 (1.22–5.66) 0.012

    No Reference

NLR

    >3.13 4.60 (1.70–12.39) 0.001

    ≤3.13 Reference

ALC

    <1,000/µL 4.41 (2.03–9.58) <0.001

    ≥1,000/µL Reference

D-dimer

    >500 ng/mL 16.97 (3.95–72.83) <0.001

    ≤500 ng/mL Reference

CRP

    >10 mg/dL 4.61 (2.11–10.04) <0.001

    ≤10 mg/dL Reference

RBG

    ≥200 mg/dL 2.78 (1.16–6.67) 0.018

    <200 mg/dL Reference

Creatinine

    >1.2 mg/dL 5.66 (2.58–12.40) <0.001

    ≤1.2 mg/dL Reference

Treatment – Chloroquine

    Yes 2.33 (1.07–5.08) 0.030

    No Reference

COVID-19 = coronavirus disease 2019, OR = odds ratio, CI = confidence interval, HFNC = high-flow nasal cannula, IMV = invasive mechanical ventilation, NRM = non-rebreather mask, WBC = white blood cells, RDW = red cell distribution width, NLR = neutrophil-to-lymphocyte ratio, ALC = absolute lymphocyte count, CRP = C-reactive protein, RBG = random blood glucose.

Table 4

Multivariate logistic regression analysis of prognostic factors for mortality in COVID-19.


PROGNOSTIC FACTORS OVERALL MORTALITY

OR (95% CI) P VALUE

Age > 45 year 1.26 (0.30–5.28) 0.754

Dyspnea 1.58 (0.47–5.25) 0.458

Hypertension 3.59 (1.12–11.48) 0.031

Diabetes mellitus 0.69 (0.16–2.97) 0.617

Obesity 6.34 (1.68–23.98) 0.007

Oxygen – nasal cannula up to NRM 2.99 (0.54–16.42) 0.207

Oxygen – HFNC and/or IMV 64.93 (11.08–380.61) <0.001

Cardiomegaly 1.22 (0.31–4.82) 0.781

Abnormal WBC count 1.96 (0.55–7.06) 0.301

Abnormal RDW 3.68 (1.09–12.34) 0.035

NLR > 3.13 0.48 (0.10–2.20) 0.344

ALC < 1000/µL 3.51 (1.08–11.44) 0.038

D–dimer > 500 ng/mL 9.36 (1.53–57.12) 0.015

CRP > 10 mg/dL 1.03 (0.30–3.56) 0.961

RBG ≥ 200 mg/dL 3.24 (0.76–13.92) 0.113

Creatinine > 1.2 mg/dL 1.85 (0.56–6.16) 0.316

Treatment – Chloroquine 3.61 (1.09–11.99) 0.036

COVID-19 = coronavirus disease 2019, OR = odds ratio, CI = confidence interval, HFNC = high-flow nasal cannula, IMV = invasive mechanical ventilation, NRM = non-rebreather mask, WBC = white blood cells, RDW = red cell distribution width, NLR = neutrophil-to-lymphocyte ratio, ALC = absolute lymphocyte count, CRP = C-reactive protein, RBG = random blood glucose.

Overall mortality rates of COVID-19 according to the presence of prognostic factors
Figure 2 

Overall mortality rates of COVID-19 according to the presence of prognostic factors.

COVID-19 = coronavirus disease 2019.

Discussion

Jakarta is the capital of Indonesia and currently has the highest mortality rate and most confirmed cases of COVID-19 in the country [15]. Moreover, it is the most populous region in Indonesia. Koja General Hospital is one of the first hospitals appointed by the local government to become a COVID-19 referral center in the area. The hospital is located in a densely populated area of Koja in the North Jakarta district which has the lowest Human Development Index among the capital’s main districts [16].

Among the 243 patients with COVID-19, the in-hospital case fatality rate was 13.7% in this study.

We showed that the presence of hypertension, obesity, immediate need of HFNC and/or IMV, abnormal RDW, ALC < 1,000/µL, D-dimer > 500 ng/mL, and chloroquine treatment were independent predictors of mortality in hospitalized adult COVID-19 patients. To our knowledge, this is one of the first studies to evaluate the prognostic factors of mortality in COVID-19 in Indonesia. It is unique in examining the combinations of demographic, clinical, laboratory, and radiological characteristics and their associations with death.

Studies from multiple countries have reported evidence that underlying cardiometabolic conditions may be associated with worse prognosis of COVID-19 [2, 17, 18, 19, 20]. Our findings supported those of previous reports from other countries, particularly hypertension and obesity, as they were associated with mortality. Diabetes was also found to be in a significantly higher proportion in our death group. However, different from other studies, it was not associated with mortality after adjustment with other prognostic factors. Cardiometabolic diseases, including hypertension, diabetes mellitus and obesity, are associated with diminished innate and adaptive immune response [21, 22, 23]. They are also linked with endothelial dysfunction and persistent low-grade inflammation [18]. Obesity reduces baseline pulmonary function and ventilatory reserve, which could predispose to worse COVID-19 outcomes [24]. The association between angiotensin-converting enzyme 2 (ACE2) expression and hypertension may also partly explain the high prevalence of severe COVID-19 in hypertensive patients [6]. Biologic plausibility is further supported by the unusual harms of COVID-19 related to vascular endothelial cells, in the lungs and throughout the body [25]. Overall, individuals with cardiometabolic conditions are likely predisposed to higher risks of lung injury, cytokine storm, and respiratory failure from COVID-19 infection [19, 24].

These conditions also promote prothrombotic milieu as the basis for coagulopathy found in COVID-19 patients [26]. Furthermore, hypoxia-mediated hyperviscocity may also aggravate thrombosis [25, 27]. Vascular injury, along with the hypercoagulability state, may aggravate the risk of cardiac injury and thus further demonstrate COVID-19 and its relationship with the heart. An increase of D-dimer level in COVID-19 patients, both at admission and during hospitalization, has been linked with increased mortality and admission to critical care [25, 28]. Our study also suggested the same finding, as D-dimer > 500 ng/mL was one of the strongest predictors of mortality among other available factors.

The significant difference in immediate need of oxygen supplementation between survivors and non-survivors in our study indicates this factor is associated with the severity of illness. Interestingly, our multivariate analysis showed only the immediate need of HFNC and/or IMV as prognostic factor for mortality. Both HFNC and IMV were used in our hospital for patients with respiratory failure. Our findings confirmed reports from previous studies that the need for mechanical ventilation was associated with high mortality in COVID-19 patients [29, 30, 31, 32]. Profound hypoxemia from respiratory failure enhances various cytotoxic functions of neutrophils and can promote hyperinflammation. Thus, it not only represents a consequence of respiratory disease but also contributes significantly to progressive lung damage after establishment of the initial injury [33, 34].

Our hematological findings showed higher RDW and lymphopenia as independent predictors for mortality. RDW reflects the heterogeneity in the volume of circulating erythrocytes. In critically ill patients with sepsis, baseline RDW has been shown to be a significant and independent predictor of mortality [35]. Consistent with our findings, numerous studies have reported the association of elevated RDW with mortality in the context of COVID-19 [36, 37, 38]. The exact mechanism behind the association has yet to be elucidated. Multiple theoretically viable hypotheses can be made to justify the prognostic role of RDW in COVID-10, including direct cytopathic injury due to infection of circulating erythrocytes, indirect erythrocyte damage consequent to intravascular coagulopathy, dysfunctional hematopoiesis due to hyperinflammatory state, and profound disturbance of iron metabolism due to the sustained inflammatory response [36, 39].

Lymphopenia is the most common abnormality on the complete blood count in COVID-19 patients [14, 25]. Low lymphocytes are also associated with poor prognosis, with lymphocyte percentage <10% on the WBC differential is strongly associated with decreased survival [40]. A recent meta-analysis proposed that lymphopenia is an important hematological signal of severe COVID-19 and could be a practical parameter to predict severe outcomes [41]. Numerous possible explanations for lymphopenia in COVID-19 have been proposed, including destruction of lymphocytes via angiotensin-converting enzyme 2 (ACE2) receptor, lymphatic organ damage, acidemia, bone marrow suppression, and cytokine storm [28, 40, 42].

Chloroquine is an anti-malarial 4-aminoquinoline shown to have in vitro activity against SARS-CoV-2 and may have beneficial immunomodulatory effects in vivo [43, 44]. An initial report from Gao et al. described the superiority of chloroquine for the treatment of COVID-19-associated pneumonia, compared to the control treatment, in 100 patients enrolled from 10 hospitals in China [45]. Further related study was not available yet. On the other hand, hydroxychloroquine was preferable in other studies due to its overall efficacy and safety. Still, data from previous observational studies were still inconsistent. Two large retrospective observational studies of hospitalized patients with COVID-19 reported no significant reduction in risk of in-hospital mortality for those who received hydroxychloroquine when compared to control [46, 47]. Conversely, another large retrospective cohort study reported a survival benefit among hospitalized patients who received hydroxychloroquine compared to those who did not [48]. However, a substantially higher percentage of patients in the hydroxychloroquine arms also received corticosteroids (77.1% of patients in the hydroxychloroquine arms vs. 36.5% of patients in the control arm). This imbalance in corticosteroid use may confounded the findings as steroids were reported to improve the survival rate of patient with COVID-19 in the Randomised Evaluation of COVID-19 Therapy (RECOVERY) trial [49].

Our study found that the use of chloroquine was associated with in-hospital death of hospitalized COVID-19 patients. The drug was given in the form of generic chloroquine phosphate (a 250-mg tablet containing a 150-mg base equivalent) two tablets every 12 hours for 5 consecutive days. No corticosteroid was given to our patients. Several studies have reported their concerns regarding the adverse effect of this 4-aminoquinoline drug, most notably cardiovascular toxicity, i.e., QT prolongation with an increased risk of cardiac complications in an already vulnerable population [47, 50]. In RECOVERY trial, the patients who received hydroxychloroquine had a longer median hospital stay and, among those who were not on invasive mechanical ventilation at the time of randomization, a higher risk of invasive mechanical ventilation or death than those who received the standard of care [51]. In another randomized controlled trial (RCT) among hospitalized patients with mild to moderate COVID-19 in Brazil, more adverse events occurred among patients who received hydroxychloroquine among those who received the standard of care [52]. An RCT of hospitalized patient with severe COVID-19 was discontinued early when preliminary results showed higher rates of mortality and QT prolongation in association with higher dose of chloroquine treatment [53].

The strength of this study is that even though this study only includes one hospital in Jakarta, Koja General Hospital is also one of the capital city referral hospital. Therefore, there are many COVID-19 patients hospitalized with different demographics that represent general population. However, in the first few months of COVID-19 pandemic in Indonesia, some patients died in the hospital with inconclusive PCR results due to the overwhelmed diagnostic facility, thus their role might be underestimated in predicting in-hospital death. A national-scale cohort study should be done to address this limitation and to obtain validation of the prognostic factors.

In conclusion, we identified seven independent predictors of mortality in hospitalized adult COVID-19 patients in Jakarta: hypertension, obesity, immediate need of HFNC and/or IMV, abnormal RDW, ALC < 1,000/µL, D-dimer > 500 ng/mL, and chloroquine treatment were independent predictors of mortality in hospitalized adult COVID-19 patients. The likelihood of mortality increased with increasing number of prognostic factors. These findings could provide valuable insight for Indonesia and other LMICs to establish effective strategies for detecting high-risk patients as early as possible and distributing healthcare resources effectively.

Data Accessibility Statement

Datasets of clinical and laboratory data presented in the current study are available from the corresponding author on reasonable request.

Acknowledgements

We thank everyone involved in the COVID-19 management and treatment team from Koja General Hospital, Jakarta, Indonesia. Particularly, we thank Adam Fathony, MD; Adhy Nalagiri Silavatto, MD; Cleine Michaela, MD; Edi Setiawan, MD; Eric Hermansyah, MD; Eufrata Silvestris Junus, MD; Hendra Dwi Kurniawan, MD; Indry Putri Festari, MD; Irfan Ferdinand, MD; Luly Nur El Wally, MD; Mochamad Okyana Bagja Suwala, MD; Natasja Rosa Munde, MD; Rahmat Hidayat, MD; Riyanda Akbar, MD; Samuel Panjaitan, MD; Sandy, MD; Sora Kerova, MD; and Sri Feliciani, MD for supporting the data collection, and Nurfanida Librianty, MD for the interpretation of chest X-ray. We also thank Jessica Marsigit, MD for the scientific advice.

Competing Interests

The authors have no competing interest to declare.

Author Contributions

BBS, AP, SR, RN, and PM contributed equally to study conception and design. AP, SR, NY, JS, and WPS contributed to data collection and drafting of the manuscript. AP, RN, PM, and BBS contributed to data analysis and data interpretation. RN, PM, and BBS revised the final manuscript. All authors have read and approved the manuscript.

References

  1. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020; 20(5): 533–4. DOI: https://doi.org/10.1016/S1473-3099(20)30120-1 

  2. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a Report of 72314 Cases From th eChinese Center for Disease Control and Prevention. JAMA. 2020; 323(13): 1239–42. DOI: https://doi.org/10.1001/jama.2020.2648 

  3. Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet. 2020; 395(10229): 1054–62. DOI: https://doi.org/10.1016/S0140-6736(20)30566-3 

  4. Chen TL, Dai Z, Mo P, et al. Clinical Characteristics and Outcomes of Older Patients with Coronavirus Disease 2019 (COVID-19) in Wuhan, China: A Single-Centered, Retrospective Study. J Gerontol A Biol Sci Med Sci. 2020; 75(9): 1788–95. DOI: https://doi.org/10.1093/gerona/glaa089 

  5. Zhang J, Wang X, Jia X, et al. Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China. Clin Microbiol Infect. 2020; 26(6): 767–72. DOI: https://doi.org/10.1016/j.cmi.2020.04.012 

  6. Li X, Xu S, Yu M, et al. Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. J Allergy Clin Immunol. 2020; 146(1): 110–8. DOI: https://doi.org/10.1016/j.jaci.2020.04.006 

  7. Phua J, Faruq MO, Kulkarni AP, et al. Critical care bed capacity in Asian countries and regions. Crit Care Med. 2020; 48(5): 654–62. DOI: https://doi.org/10.1097/CCM.0000000000004222 

  8. Sethuraman N, Jeremiah SS, Ryo A. Interpreting Diagnostic Tests for SARS-CoV-2. JAMA – J Am Med Assoc. 2020; 323(22): 2249–51. DOI: https://doi.org/10.1001/jama.2020.8259 

  9. Burhan E, Syam AF, Rahyussalim AJ, et al. The emergence of COVID-19 in Indonesia: Analysis of predictors of infection and mortality using independent and clustered data approaches. medRxiv; 2020. DOI: https://doi.org/10.1101/2020.07.10.20147942 

  10. WHO/IASO/IOTF. The Asia-Pacific perspective: Redefining obesity and its treatment. Melbourne: Health Communications Australia; 2000. 

  11. Chobanian AV, Bakris GL, Black HR, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: The JNC 7 Report. JAMA. 2003; 289: 2560–72. DOI: https://doi.org/10.1001/jama.289.19.2560 

  12. Liu J, Liu Y, Xiang P, et al. Neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage. J Transl Med. 2020; 18(1): 1–12. DOI: https://doi.org/10.1186/s12967-020-02374-0 

  13. Wagner J, DuPont A, Larson S, Cash B, Farooq A. Absolute lymphocyte count is a prognostic marker in Covid-19: A retrospective cohort review. Int J Lab Hematol. 2020; 42(6): 761–5. DOI: https://doi.org/10.1111/ijlh.13288 

  14. Guan W, Ni Z, Hu Y, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020; 382(18): 1708–20. DOI: https://doi.org/10.1056/NEJMoa2002032 

  15. World Health Organization Indonesia. Coronavirus Disease 2019 (COVID-19) Situation Report 43 [Internet]. 2021. Retrieved from: https://cdn.who.int/media/docs/default-source/searo/indonesia/covid19/external-situation-report-43_17-february.pdf?sfvrsn=1889cdf9_5 (accessed 23 February 2021). 

  16. Widarta S. (ed.) Indeks Pembangunan Manusia DKI Jakarta 2020. Jakarta: Badan Pusat Statistik Provinsi DKI Jakarta; 2020. 

  17. Richardson S, Hirsch JS, Narasimhan M, et al. Presenting Characteristics, Comorbidities, and Outcomes among 5700 Patients Hospitalized with COVID-19 in the New York City Area. JAMA – J Am Med Assoc. 2020; 323(20): 2052–9. DOI: https://doi.org/10.1001/jama.2020.6775 

  18. Svensson P, Hofmann R, Häbel H, Jernberg T, Nordberg P. Association between cardiometabolic disease and severe COVID-19: A nationwide case-control study of patients requiring invasive mechanical ventilation. BMJ Open. 2021; 11(2): 1–10. DOI: https://doi.org/10.1136/bmjopen-2020-044486 

  19. O’hearn M, Liu J, Cudhea F, Micha R, Mozaffarian D. Coronavirus disease 2019 hospitalizations attributable to cardiometabolic conditions in the united states: A comparative risk assessment analysis. J Am Heart Assoc. 2021; 10(5): 1–27. DOI: https://doi.org/10.1161/JAHA.120.019259 

  20. De Almeida-Pititto B, Dualib PM, Zajdenverg L, et al. Severity and mortality of COVID 19 in patients with diabetes, hypertension and cardiovascular disease: A meta-analysis. Diabetol Metab Syndr [Internet]. 2020; 12(1): 1–12. DOI: https://doi.org/10.1186/s13098-020-00586-4 

  21. Geerlings SE, Hoepelman AIM. Immune dysfunction in patients with diabetes mellitus (DM). FEMS Immunol Med Microbiol. 1999; 26(3–4): 259–65. DOI: https://doi.org/10.1111/j.1574-695X.1999.tb01397.x 

  22. Andersen CJ, Murphy KE, Fernandez ML. Impact of obesity and metabolic syndrome on immunity. Adv Nutr. 2016; 7(1): 66–75. DOI: https://doi.org/10.3945/an.115.010207 

  23. Singh MV, Chapleau MW, Harwani SC, Abboud FM. The immune system and hypertension. Immunol Res. 2014; 59(1–3): 243–53. DOI: https://doi.org/10.1007/s12026-014-8548-6 

  24. Sharifi Y, Payab M, Mohammadi-Vajari E, et al. Association between cardiometabolic risk factors and COVID-19 susceptibility, severity and mortality: A review. J Diabetes Metab Disord. 2021; (0123456789). DOI: https://doi.org/10.1007/s40200-021-00822-2 

  25. Gupta A, Madhavan MV, Sehgal K, et al. Extrapulmonary manifestations of COVID-19. Nat Med. 2020; 26(7): 1017–32. DOI: https://doi.org/10.1038/s41591-020-0968-3 

  26. Lillicrap D. Disseminated intravascular coagulation in patients with 2019-nCoV pneumonia. J Thromb Haemost. 2020; 18(4): 786–7. DOI: https://doi.org/10.1111/jth.14781 

  27. Paz LO, Capodanno D, Montalescot G, Angiolillo DJ. Coronavirus Disease 2019 – associated thrombosis and coagulopathy: Review of the pathophysiological characteristics and implications for antithrombotic management. J Am Hear Assoc. 2021; 10: e019650. DOI: https://doi.org/10.1161/JAHA.120.019650 

  28. Terpos E, Ntanasis-Stathopoulos I, Elalamy I, et al. Hematological findings and complications of COVID-19. Am J Hematol. 2020; 95(7): 834–47. DOI: https://doi.org/10.1002/ajh.25829 

  29. Bonnet N, Martin O, Boubaya M, et al. High flow nasal oxygen therapy to avoid invasive mechanical ventilation in SARS-CoV-2 pneumonia: A retrospective study. Ann Intensive Care. 2021; 11(1): 1–9. DOI: https://doi.org/10.1186/s13613-021-00825-5 

  30. King CS, Sahjwani D, Brown AW, et al. Outcomes of mechanically ventilated patients with COVID-19 associated respiratory failure. PLoS One. 11 November 2020; 15: 1–9. DOI: https://doi.org/10.1371/journal.pone.0242651 

  31. Bahl A, Van Baalen MN, Ortiz L, et al. Early predictors of in-hospital mortality in patients with COVID-19 in a large American cohort. Intern Emerg Med. 2020; 15(8): 1485–99. DOI: https://doi.org/10.1007/s11739-020-02509-7 

  32. Oliveira E, Parikh A, Lopez-Ruiz A, et al. ICU outcomes and survival in patients with severe COVID-19 in the largest health care system in central Florida. PLoS One. 3 March 2021; 16: 1–14.DOI: https://doi.org/10.1371/journal.pone.0249038 

  33. Eltzschig HK, Carmeliet P. Hypoxia and inflammation. N Engl J Med. 2011; 364(7): 656–65. DOI: https://doi.org/10.1056/NEJMra0910283 

  34. Mejía F, Medina C, Cornejo E, et al. Oxygen saturation as a predictor of mortality in hospitalized adult patients with COVID-19 in a public hospital in Lima, Peru. PLoS One. 2020; 15(12): 1–12. DOI: https://doi.org/10.1371/journal.pone.0244171 

  35. Zhang L, Yu CH, Guo KP, Huang CZ, Mo LY. Prognostic role of red blood cell distribution width in patients with sepsis: A systematic review and meta-analysis. BMC Immunol. 2020; 21(1): 1–8. DOI: https://doi.org/10.1186/s12865-020-00369-6 

  36. Foy BH, Carlson JCT, Reinertsen E, et al. Association of red blood cell distribution width with mortality risk in hospitalized adults with SARS-CoV-2 infection. JAMA Netw Open. 2020; 3(9): 1–13. DOI: https://doi.org/10.1001/jamanetworkopen.2020.22058 

  37. Karampitsakos T, Akinosoglou K, Papaioannou O, et al. Increased red cell distribution width Is associated with disease severity in hospitalized adults with SARS-CoV-2 Infection: An observational multicentric study. Front Med. 2020 December; 7: 3–6. DOI: https://doi.org/10.3389/fmed.2020.616292 

  38. Soni M, Gopalakrishnan R. Significance of RDW in predicting mortality in COVID-19—An analysis of 622 cases. Int J Lab Hematol. 2021 March; 1–3. DOI: https://doi.org/10.1111/ijlh.13526 

  39. Henry BM, Benoit JL, Benoit S, et al. Red blood cell distribution width (RDW) predicts COVID-19 severity: A prospective, observational study from the Cincinnati SARS-CoV-2 Emergency Department cohort. Diagnostics. 2020; 10(9): 1–9. DOI: https://doi.org/10.3390/diagnostics10090618 

  40. Tan L, Wang Q, Zhang D, et al. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal Transduct Target Ther. 2020; 5: 33. DOI: https://doi.org/10.1038/s41392-020-0148-4 

  41. Zhao Q, Meng M, Kumar R, Wu Y, Huang J. Lymphopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: A systemic review and meta-analysis. Int J Infect Dis. 2020; 96: 131–5. DOI: https://doi.org/10.1016/j.ijid.2020.04.086 

  42. Fathi N, Rezaei N. Lymphopenia in COVID-19: Therapeutic opportunities. Cell Biol Int. 2020; 44(9): 1792–7. DOI: https://doi.org/10.1002/cbin.11403 

  43. Sanders JM, Monogue ML, Jodlowski TZ, Cutrell JB. Pharmacologic Treatments for Coronavirus Disease 2019 (COVID-19): A Review. JAMA – J Am Med Assoc. 2020; 323(18): 1824–36. DOI: https://doi.org/10.1001/jama.2020.6019 

  44. Wang M, Cao R, Zhang L, et al. Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Res. 2020; 30(3): 269–71. DOI: https://doi.org/10.1038/s41422-020-0282-0 

  45. Gao J, Tian Z, Yang X. Breakthrough: Chloroquine phosphate has shown apparent efficacy in treatment of COVID-19 associated pneumonia in clinical studies. Biosci Trends. 2020; 14(1): 1–2. DOI: https://doi.org/10.5582/bst.2020.01047 

  46. Geleris J, Sun Y, Platt J, et al. Observational Study of Hydroxychloroquine in Hospitalized Patients with Covid-19. N Engl J Med. 2020; 382(25): 2411–8. DOI: https://doi.org/10.1056/NEJMoa2012410 

  47. Rosenberg ES, Dufort EM, Udo T, et al. Association of Treatment with Hydroxychloroquine or Azithromycin with In-Hospital Mortality in Patients with COVID-19 in New York State. JAMA – J Am Med Assoc. 2020; 323(24): 2493–502. DOI: https://doi.org/10.1001/jama.2020.8630 

  48. Arshad S, Kilgore P, Chaudhry ZS, et al. Treatment with hydroxychloroquine, azithromycin, and combination in patients hospitalized with COVID-19. Int J Infect Dis. 2020; 97: 396–403. DOI: https://doi.org/10.1016/j.ijid.2020.06.099 

  49. The RECOVERY Collaborative Group. Dexamethasone in Hospitalized Patients with Covid-19 — Preliminary Report. N Engl J Med. 2020; 1–11. 

  50. Mahévas M, Tran VT, Roumier M, et al. Clinical efficacy of hydroxychloroquine in patients with covid-19 pneumonia who require oxygen: Observational comparative study using routine care data. BMJ. 2020; 369: m1884. 

  51. The RECOVERY Collaborative Group. Effect of Hydroxychloroquine in Hospitalized Patients with Covid-19. N Engl J Med. 2020; 383(21): 2030–40. DOI: https://doi.org/10.1056/NEJMoa2022926 

  52. Cavalcanti AB, Zampieri FG, Rosa RG, et al. Hydroxychloroquine with or without Azithromycin in Mild-to-Moderate Covid-19. N Engl J Med. 2020; 383(21): 2041–52. DOI: https://doi.org/10.1056/NEJMoa2019014 

  53. Borba MGS, Val FFA, Sampaio VS, et al. Effect of High vs Low Doses of Chloroquine Diphosphate as Adjunctive Therapy for Patients Hospitalized With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection: A Randomized Clinical Trial. JAMA Netw Open. 2020; 3(4.23): e208857. DOI: https://doi.org/10.1001/jamanetworkopen.2020.8857 

comments powered by Disqus