Evidence map›Paper›PMID 32647419›Full record

ReviewIndian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion2020

Risk Stratification in Multiple Myeloma in Indian Settings.

Rajan Kapoor, Rajiv Kumar, A P Dubey

Open access · bronzeAbstract readReview
In one paragraph

Review in Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 1 country.

Rajan KapoorMedicine and Clinical Hematology, Command Hospital (EC), Kolkata, India.
Rajiv KumarDepartment of Medicine and Clinical Hematology, INHS Asvini, R C Church, Colaba, Mumbai, 400005 India.ORCID 0000-0002-8688-1886
A P DubeyMedical Oncology, Clear Medi Hospitals and Cancer Centre, Ghaziabad, UP India.
Cancer Institute (WIA) · INCommand Hospital · ININHS Asvini · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple myeloma (MM) constitutes 10% of all hematological malignancies. The last one decade has seen a phenomenal progress in the therapeutic options available for the management. Although it still remains incurable, with the advent of newer therapies, the median survival in many risk groups is now around 10 years. Conventional karyotyping of bone marrow samples has a positivity rate of 20-30% at diagnosis in patients of Multiple Myeloma. However, array Comparative Genomic Hybridisation (aCGH) has revealed that almost all MM patients have cytogenetic abnormalities which may affect the pathophysiology, selection of therapy and outcomes of the disease. The progress in the field of exploring the genetic landscape of multiple myeloma with multiple tools like Fluorescent in-situ hybridization, aCGH, Next Generation Sequencing, Flow cytometry, etc., combined with the traditional risk stratification markers like albumin, β2 microglobulin and LDH, is gradually leading towards a risk-adapted therapy. The recent R-ISS risk stratification has combined these two group of information to validate a prognostic score which is an improvement over the past tools like DSS and ISS. In view of the plethora of information available on the multitude of cytogenetic markers there is a tendency to evaluate for all of them at diagnosis, especially in research centers. This leads to a significant increase in the cost of therapy of Multiple Myeloma in day-to-day clinical practice and an increased out-of-pocket spending to the patient, especially in resource-limited settings like India. Also, there is a variable approach to pre-therapy cytogenetic evaluation and risk stratification at different Hematology centres in the country, often dictated by financial constraints and availability of specialized tests. This review discusses the risk stratification markers and tools available in MM in 2019 and how it can be adapted in the resource constraint settings so as to derive the maximum prognostic information from a minimal prognostic panel, as well as lead to standardization of the prognostic protocols in resource limited settings across various Hematology centres in India.

Indexed as

CytogeneticsMyelomaPrognosisRisk stratification

Identifiers

PMID32647419
PMCPMC7326869
OpenAlexW2995742466

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.