Combined immune score predicts the prognosis of newly diagnosed multiple myeloma patients in the bortezomib-based therapy era

To investigate the effect of a combined immune score including the lymphocyte-to-monocyte ratio (LMR) and uninvolved immunoglobulin (u-Ig) levels on the prognosis of newly diagnosed multiple myeloma (NDMM) patients treated with bortezomib.Clinical data of 201 NDMM patients were retrospectively analyzed. Patients with LMR ≥ 3.6 and LMR < 3.6 were scored 0 and 1, respectively. Patients with preserved u-Ig levels, suppression of 1 u-Ig, and suppression of at least 2 u-Igs were scored 0, 1, and 2, respectively. The immune score, established from these individual scores, was used to separate patients into good (0-1 points), intermediate (2 points), and poor (3 points) risk groups. The baseline data, objective remission rate (ORR), whether receive maintenance treatment regularly and overall survival of patients before treatment were analyzed.The ORR of the good-risk group was significantly higher than that of the intermediate-risk group (75.6% vs 57.7%, P = .044) and the poor-risk group (75.6% vs 48.2%, P = .007). The multivariate analysis results showed that age ≥ 65 years, International Staging System stage III, platelet count ≤ 100 × 109/L, lactate dehydrogenase (LDH) > 250 U/L, serum calcium > 2.75 mmol/L, no receipt of regular maintenance treatment, LMR < 3.6, suppressed u-Igs = 1, suppressed u-Igs ≥ 2, intermediate-risk group and poor-risk group were independent predictors of poor overall survival.In the bortezomib era, the LMR, u-Ig levels, and the immune score play an important role in the prognosis of NDMM patients. Among them, the immune score showed the strongest prognostic value, and it could be a beneficial supplement for the early identification of high-risk patients.

Covid-19 and diabetes in primary care - How do hematological parameters present in this cohort?

Objectives: Changes in hematological parameters are becoming evident as important early markers of COVID-19. Type 2 Diabetes Mellitus (T2DM) has been shown to be associated with increased severity of COVID-19. In this study, we aim to explore the various hematological variables in COVID-19 positive patients with T2DM, so as to act early and improve patient outcomes.Methods: Medical e-records of seventy adult patients with T2DM who were COVID-19 positive have been analyzed in this retrospective cohort study. Demographic, clinical and laboratory parameters for these patients were examined.Results: Of the seventy patients with T2DM, 48.88% had poorly controlled diabetes. 70.69% were pyrexial, 56.25% were tachycardic and 38.58% were asymptomatic on presentation. Amongst the hematological parameters, anemia was seen in 10% of males and 15.38% of females. 20% had a high red-blood-cell-distribution-width (RDW). 7.27% had thrombocytosis and 3.64% had thrombocytopenia. 73.3% had a high platelet-distribution-width (PDW) and 44.44% had an increased mean-platelet-volume (MPV). 16.36% were neutropenic and 16.67% had lymphocytopenia.Conclusion: Diabetic COVID-19 positive patients have been shown to have prominent manifestations of the hemopoietic-system with varied hematological profiles. Recognizing the implications of these variables early in primary-care, can help clinicians aid management decisions and dictate early referral to secondary-care services, to help improve prognosis.

Baseline clinical characteristics and prognostic factors in hospitalized COVID-19 patients aged ≤ 65 years: A retrospective observational study

BACKGROUND

Individual differences in susceptibility to SARS-CoV-2 infection, symptomatology and clinical manifestation of COVID-19 have thus far been observed but little is known about the prognostic factors of young patients.

METHODS

A retrospective observational study was conducted on 171 patients aged ≤ 65 years hospitalized in Alessandria's Hospital from 1st March to 30th April 2020 with laboratory confirmed COVID-19. Epidemiological data, symptoms at onset, clinical manifestations, Charlson Comorbidity Index, laboratory parameters, radiological findings and complications were considered. Patients were divided into two groups on the basis of COVID-19 severity. Multivariable logistic regression analysis was used to establish factors associated with the development of a moderate or severe disease.

FINDINGS

A total of 171 patients (89 with mild/moderate disease, 82 with severe/critical disease), of which 61% males and a mean age (± SD) of 53.6 (± 9.7) were included. The multivariable logistic model identified age (50-65 vs 18-49; OR = 3.23 CI95% 1.42-7.37), platelet count (per 100 units of increase OR = 0.61 CI95% 0.42-0.89), c-reactive protein (CPR) (per unit of increase OR = 1.12 CI95% 1.06-1.20) as risk factors for severe or critical disease. The multivariable logistic model showed a good discriminating capacity with a C-index value of 0.76.

INTERPRETATION

Patients aged ≥ 50 years with low platelet count and high CRP are more likely to develop severe or critical illness. These findings might contribute to improved clinical management.

Continuing developments with the automated platelet count1

The four main procedures for platelet counting are: manual phase contrast microscopy, impedance, optical light scatter/fluorescence and flow cytometry. Early methods to enumerate platelets were inaccurate and irreproducible. The manual count is still recognized as the gold standard or reference method, and until very recently the calibration of platelet counts by the manufacturers of automated cell counters and quality control material was performed by this method. However, it is time-consuming and results in high levels of imprecision. The introduction of automated full blood counters using impedance technology resulted in a dramatic improvement in precision. However, impedance counts still have limitations as cell size analysis cannot discriminate platelets from other similar-sized particles. More recently, light scatter or fluorescence methods have been introduced for automated platelet counting, but there are still occasional cases where an accurate platelet count remains a challenge. Thus, there has been interest in the development of an improved reference procedure to enable optimization of automated platelet counting. This method utilizes monoclonal antibodies to platelet cell surface antigens conjugated to a suitable fluorophore. This permits the possible implementation of a new reference method to calibrate cell counters, assign values to calibrators, and to obtain a direct platelet count on a variety of pathological samples. In future, analysers may introduce additional platelet parameters; a reliable method to quantify immature or reticulated platelets would be useful.