Introduction: COVID-19 is a pandemic with significant mortality and it is important to differentiate severe and non-severe cases. We conducted a study to evaluate hematologic profiles with inflammation markers in COVID-19 patients and to determine the correlation of neutrophil-lymphocyte ratio (NLR) with disease severity. Methods: A cross-sectional study involving hospitalized COVID-19 patients confirmed with a positive SARS-CoV-2 PCR test in Dr. Cipto Mangunkusumo Hospital. Lymphocyte count, NLR, C-reactive protein (CRP) and ferritin were evaluated in severe and non-severe COVID-19 cases at hospital admission. Data was analyzed using Spearman correlation. Results: There were 41 patients aged 20 to 79 years with COVID-19; 33 (80.5%) were non-severe, and 8 (19.5%) were severe cases. There is a statistically significant difference in WBC, relative neutrophils and lymphocytes, NLR, and CRP between non-severe and severe cases. There is a strong correlation between NLR and CRP (r = 0.738; p < 0.001). Our findings show that NLR and absolute lymphocyte count, but not ferritin, play a role in differentiating between non-severe and severe COVID-19 cases. Conclusion: In COVID-19 cases, a strong correlation between NLR and CRP might suggest the use of NLR to differentiate between non-severe and severe cases, especially in a remote healthcare facility.

Background: Coagulopathy and inflammation are associated with coronavirus disease 2019 (COVID-19) severity. This study assessed D-dimer concentration and its correlation with inflammatory markers and COVID-19 severity. Methods: This was a retrospective cross-sectional study involving 194 COVID-19 cases, with the severity of infection graded in accordance with the World Health Organization (WHO) guidelines. We measured D-dimer, C-reactive protein (CRP), and ferritin on admission and determined the cutoff values for D-dimer and CRP and evaluated the correlation between D-dimer and CRP and ferritin. Results: Median D-dimer, CRP, and ferritin concentrations were 2240 µg/L, 73.2 mg/L, and 1173.8 µg/mL, respectively. The highest median D-dimer value was seen in mild and moderate acute respiratory distress syndrome (ARDS). The highest ferritin concentration was seen in severe ARDS. There was a significant correlation between D-dimer value and CRP (r = 0.327), but no significant correlation between D-dimer and ferritin (r = 0.101). The area under the receiver operating characteristic curve (AUC) for the combination of CRP ≥72.65 mg/L and D-dimer ≥1250 µg/L as a marker of COVID-19 severity was 0.722 (95% confidence interval (CI): 0.615–0.781). Conclusion: The combination of CRP ≥72.65 mg/L and D-dimer ≥1250 µg/L can be used as marker of COVID-19 severity, with moderate accuracy.

The current study aimed to know procalcitonin levels in patients with metastatic tumor, and to discover the cut-off point for sepsis in this population. A cross-sectional study was conducted with patients with solid tumor. Sepsis and systemic inflammation response syndrome (SIRS) were identified using clinical, laboratory, and microbiological criteria. The cut-off point was determined using receiver operating characteristic (ROC) curve. A total of 112 subjects enrolled in this study, 51% male, mean age 47.9 ± 12.47 years. Among 71 (63.4%) patients who had metastasis, 36 (32.1%) had sepsis and 6 (5.3%) experienced SIRS. In the absence of sepsis, the procalcitonin levels were significantly higher in patients with metastatic tumor compared to those without [0.25 ng/mL (0.07–1.76) vs. 0.09 ng/mL (0.03–0.54); p < 0.001]. The ROC curve showed that levels of procalcitonin for sepsis in metastatic solid tumors were in the area under curve (AUC) [0.956; CI 0.916–0.996]. Cut-off point of procalcitonin for sepsis was 1.14 ng/mL, Sn 86%, and Sp 88%. Thus, the results show that metastatic tumor affects the patients’ procalcitonin level, even in the absence of sepsis. The cut-off point of procalcitonin level for diagnosing sepsis in the meta-static solid tumor was higher compared to the standard value.

Post-thrombotic syndrome (PTS) is a complication of deep vein thrombosis (DVT) and affects 20–40% of DVT subjects. The risk factor of PTS after DVT is difficult to determine. We aimed to evaluate the incidence of PTS after 3 months of DVT diagnosis and to determine the risk of PTS. A retrospective cohort study of subjects who developed DVT confirmed by Doppler ultrasound in Cipto Mangunkusumo Hospital from April 2014 until June 2015. The presence of PTS was assessed after 3 months of completed DVT treatment using the Villalta score. Risk factors for PTS were evaluated from medical records. There were 91 subjects with DVT with mean age of 58 years. 56% were female. It was dominated by subjects aged ≥ 60 years (45.1%). Hypertension (30.8%) and diabetes mellitus (26.4%) were the major comorbidities in this study. Deep vein thrombosis occurred commonly in unilateral side (79.1%), proximal localization (87.9%), and unprovoked DVT (47.3%). The cumulative incidence of PTS after DVT was 53.8%, 69% of subjects had mild PTS. Heaviness of the leg (63.2%) and edema (77.5%) were the most common symptoms. There were 91 subjects with DVT with mean age of 58 years. 56% were female. It was dominated by subjects aged ≥ 60 years (45.1%). Hypertension (30.8%) and diabetes mellitus (26.4%) were the major comorbidities in this study. Deep vein thrombosis occurred commonly in unilateral side (79.1%), proximal localization (87.9%), and unprovoked DVT (47.3%). The cumulative incidence of PTS after DVT was 53.8%, 69% of subjects had mild PTS. Heaviness of the leg (63.2%) and edema (77.5%) were the most common symptoms. Significant risk factors for PTS were unprovoked DVT (adjusted RR 1.67; 95%CI: 1.17–2.04; p = 0.01) and female gender (adjusted RR 1.55; 95%CI: 1.03–1.94; p = 0.04). Age, body mass index, thrombus location, immobilization, malignancy and surgery was not associated with PTS. We conclude that 53.8% of subjects suffered PTS after 3 months of DVT. Unprovoked DVT and female gender were significant risk factors for PTS.

Background: Myeloid cells express microparticles that could increase the expression of adhesion molecules including P-selectin. We aimed to evaluate the level of soluble P-selectin (sP-selectin) and prothrombin fragment 1 + 2 (F1 + 2), and to determine correlation of sP-selectin with leukocyte count and F1 + 2 levels in acute myeloid leukemia (AML) and chronic myeloid leukemia (CML) patients. Methods: Patients with newly diagnosed AML (n = 25), CML (n = 13), and controls (n = 17) were recruited in this study. The diagnosis of AML and CML is based on 2001 WHO and/or FAB criteria. Levels of sP-selectin and F1 + 2 were determined using enzyme-linked immunosorbent assay kits (Behring ELISA Processor-III® and Behring Enzygnost F1 + 2). Results: sP-selectin was significantly elevated in CML patients compared to AML patients (p = 0.001). Levels of F1 + 2 in AML and CML patients were significantly increased in comparison to controls (p < 0.001 and p = 0.043). Levels of sP-selectin were significantly correlated to leukocyte count (r = 0.437; p = 0.029) and F1 + 2 (r = 0.436; p = 0.029) in AML patients. Conclusions: AML and CML patients had an increased tendency to thrombosis. While CML patients had higher platelet and/or endothelial activation, hypercoagulable state are more pronounced in AML patients.

To investigate the correlation between glycaemic control with component of Physio-Cognitive Decline Syndrome (PCDS) and among each component of PCDS itself. A cross sectional study was conducted (January 2021–November 2022) at Dr. Cipto Mangunkusumo Hospital, Jakarta, Indonesia on consecutively recruited T2DM outpatients aged 40–59 years old. Data on the latest three months HbA1c, hand grip strength (HGS), usual gait speed (GS), and Indonesian Montreal Cognitive Assessment (MoCA-Ina) were evaluated. Pearson or Spearman's test was used to analyse the correlations. There were 133 subjects with median age 53 (40–59) years. The PCDS was found in 48.1 % subjects, of which 64.1 % with uncontrolled glycaemia. Significant correlations were found between HGS and HbA1c (r = −0.24, R² = 0.06, p < 0.01) and MoCA-Ina score (r = 0.21, R² = 0.04, p < 0.05). The higher HbA1c and the lower MoCA-Ina score, the weaker handgrip strength was.