ipssm
v1.0.4
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Javascript Package for the Molecular International Prognostic Scoring System (IPSS-M) for Myelodysplastic Syndromes.
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ipssm (js)
Javascript/Node Package for the Molecular International Prognostic Scoring System (IPSS-M) for Myelodysplastic Syndromes.
For the R package, see papaemmelab/ipssm.
For the Online Web Calculator, visit https://mds-risk-model.com.
Table of contents
- 📖 IPSS-M Publication
- 🚀 Installation instructions
- 💥 IPSS-M Usage
- 🗒️ Input Variables Definition
- ❓ Question
:page_with_curl: IPSS-M Publication
:rocket: Installation instructions
# Using npm
npm install ipssm:boom: IPSS-M Usage
:fire: Compute IPSS-M
Having a patient's data in a dictionary, you can compute the IPSS-M.
const { ipssm } from 'ipssm'
// Add patient data to an object with the following fields
const patientFields = {
// Clinical Data
BM_BLAST: 0,
HB: 8.2,
PLT: 239,
// Optional IPSS-R fields
ANC: 0.72,
AGE: 63.5,
// Cytogenetic Data
del5q: 0,
del7_7q: 0,
del17_17p: 0,
complex: 0,
CYTO_IPSSR: 'Good',
// Molecular Data
TP53mut: 0,
TP53maxvaf: 0,
TP53loh: 0,
MLL_PTD: 0,
FLT3: 0,
ASXL1: 1,
CBL: 0,
DNMT3A: 0,
ETV6: 0,
EZH2: 1,
IDH2: 0,
KRAS: 0,
NPM1: 0,
NRAS: 0,
RUNX1: 1,
SF3B1: 0,
SRSF2: 0,
U2AF1: 0,
BCOR: 0,
BCORL1: 0,
CEBPA: 0,
ETNK1: 0,
GATA2: 0,
GNB1: 0,
IDH1: 0,
NF1: 0,
PHF6: 0,
PPM1D: 0,
PRPF8: 0,
PTPN11: 0,
SETBP1: 0,
STAG2: 0,
WT1: 0,
}
const ipssmResult = ipssm(patientFields)
console.log(ipssmResult)// Result
{
means: {
riskScore: 0.09,
riskCat: 'Moderate High',
contributions: {...}
},
best: {
riskScore: 0.09,
riskCat: 'Moderate High',
contributions: {...}
},
worst: {
riskScore: 0.09,
riskCat: 'Moderate High',
contributions: {...}
},
}:zap: IPSS-R and IPSS-R (Age adjusted)
Additionally, you may find an implementation to compute the IPPS-R and IPSS-R (Age adjusted) in this module:
import { ipssr } from 'ipssm'
// using the same patient data
patientFields = {
HB: 8.2,
ANC: 0.72,
PLT: 239,
BM_BLAST: 0,
CYTOVEC: 1,
AGE: 63.5,
...
}
const ipssrResult = ipssr({
hb: patientFields.HB,
anc: patientFields.ANC,
plt: patientFields.PLT,
bmblast: patientFields.BM_BLAST,
cytovec: patientFields.CYTOVEC,
age: patientFields.AGE,
})
console.log(ipssrResult)Which outputs a risk score (means), with a best and worst scenario risk score to account for missing genetic data.
// Result
{
IPSSR_SCORE: 2.5,
IPSSR: 'Low',
IPSSRA_SCORE: 2.2563,
IPSSRA: 'Low',
}:dart: Annotating batch from CSV/Excel file
The following code will annotate a CSV file with the IPSS-M and IPSS-M Risks.
import { annotateFile } from 'ipssm'
const inputFile = './test/data/IPSSMexample.csv'
const outputFile = 'IPSSMexample.annotated.csv'
await annotateFile(inputFile, outputFile)or with an excel file:
import { annotateFile } from 'ipssm'
const inputFile = './test/data/IPSSMexample.xlsx'
const outputFile = 'IPSSMexample.annotated.xlsx'
await annotateFile(inputFile, outputFile):mechanical_arm: Using the command line interface
You can use the command line interface to annotate a file with patients, where each row is a patient and each column is a variable.
$ ipssm --help
Annotate a file of patients with IPSS-M and IPSS-R risk scores and categories.
It supports .csv, .tsv, .xlsx files.
Usage: ipssm <inputFile> <outputFile>
Positionals:
inputFile File to be annotated (rows: patients, columns: variables).[string]
outputFile Path for the annotated output file. [string]
Options:
--version Show version number [boolean]
-h, --help Show help [boolean]:spiral_notepad: Input Variables Definition
| Category | Variable Explanation | Variable | Unit | Possible Value |
|----------------------------|-------------------------------|--------------|------------------------------|-------------------------------------------------------------|
| clinical | Hemoglobin | HB | numerical, in g/dL | [4-20] |
| clinical | Platelets | PLT | numerical, in Giga/L | [0-2000] |
| clinical | Bone Marrow Blasts | BM_BLAST | numerical, in % | [0-30] |
| clinical (only for IPSS-R) | Absolute Neutrophil Count | ANC | numerical, in Giga/L | [0-15] |
| clinical (only for IPSS-RA)| Bone Marrow Blasts | AGE | numerical, in years | [18-120] |
| cytogenetics | Presence of del(5q) | del5q | binary | 0/1 |
| cytogenetics | Presence of -7/del(7q) | del7_7q | binary | 0/1 |
| cytogenetics | Presence of -17/del(17p) | del17_17p | binary | 0/1 |
| cytogenetics | Complex karyotype | complex | binary | 0/1 |
| cytogenetics | Cytogenetics Category | CYTO_IPSSR | categorical | Very Good/Good/Intermediate/Poor/Very Poor |
| TP53 locus | Number of TP53 mutations | TP53mut | categorical | 0/1/2 or more |
| TP53 locus | Maximum TP53 VAF | TP53maxvaf | numerical, between 0 and 1 | [0-1] |
| TP53 locus | Loss of heterozygosity at TP53| TP53loh | binary | 0/1 |
| MLL and FLT3 mutations | MLL PTD | MLL_PTD | binary | 0/1 |
| MLL and FLT3 mutations | FLT3 ITD or TKD | FLT3 | binary | 0/1 |
| gene main effect | ASXL1 | ASXL1 | binary | 0/1/NA |
| gene main effect | CBL | CBL | binary | 0/1/NA |
| gene main effect | DNMT3A | DNMT3A | binary | 0/1/NA |
| gene main effect | ETV6 | ETV6 | binary | 0/1/NA |
| gene main effect | EZH2 | EZH2 | binary | 0/1/NA |
| gene main effect | IDH2 | IDH2 | binary | 0/1/NA |
| gene main effect | KRAS | KRAS | binary | 0/1/NA |
| gene main effect | NPM1 | NPM1 | binary | 0/1/NA |
| gene main effect | NRAS | NRAS | binary | 0/1/NA |
| gene main effect | RUNX1 | RUNX1 | binary | 0/1/NA |
| gene main effect | SF3B1 | SF3B1 | binary | 0/1/NA |
| gene main effect | SRSF2 | SRSF2 | binary | 0/1/NA |
| gene main effect | U2AF1 | U2AF1 | binary | 0/1/NA |
| gene residual | | BCOR | binary | 0/1/NA |
| gene residual | | BCORL1 | binary | 0/1/NA |
| gene residual | | CEBPA | binary | 0/1/NA |
| gene residual | | ETNK1 | binary | 0/1/NA |
| gene residual | | GATA2 | binary | 0/1/NA |
| gene residual | | GNB1 | binary | 0/1/NA |
| gene residual | | IDH1 | binary | 0/1/NA |
| gene residual | | NF1 | binary | 0/1/NA |
| gene residual | | PHF6 | binary | 0/1/NA |
| gene residual | | PPM1D | binary | 0/1/NA |
| gene residual | | PTPN11 | binary | 0/1/NA |
| gene residual | | PRPF8 | binary | 0/1/NA |
| gene residual | | SETBP1 | binary | 0/1/NA |
| gene residual | | STAG2 | binary | 0/1/NA |
| gene residual | | WT1 | binary | 0/1/NA |
:question: Question
Any questions feel free to add an issue to this repo or to contact ElsaB.