[](https://github.com/papaemmelab/ipssm-js/actions/workflows/risk-scores-test.yml) [](https://badge.fury.io/js/ipssm) [](https://www.jsdelivr.com/package/npm/ipssm) # ipssm (js) Javascript/Node Package for the Molecular International Prognostic Scoring System (IPSS-M) for Myelodysplastic Syndromes. - For the R package, see [papaemmelab/ipssm](https://github.com/papaemmelab/ipssm). - For the Online Web Calculator, visit [https://mds-risk-model.com](https://mds-risk-model.com). ## Table of contents - [📖 IPSS-M Publication](#page_with_curl-ipss-m-publication) - [🚀 Installation instructions](#rocket-installation-instructions) - [💥 IPSS-M Usage](#boom-ipss-m-usage) - [🔥 Compute IPSS-M](#fire-compute-ipss-m) - [⚡️ IPSS-R and IPSS-R (Age adjusted)](#zap-ipss-r-and-ipss-r-age-adjusted) - [🎯 Annotating batch from CSV/Excel file](#dart-annotating-batch-from-csvexcel-file) - [🦾 Using the command line interface](#mechanical_arm-using-the-command-line-interface) - [🗒️ Input Variables Definition](#spiral_notepad-input-variables-definition) - [❓ Question](#question-question) [inputs](#inputs) ## :page_with_curl: IPSS-M Publication [Bernard E, Tuechler H, Greenberg PL, Hasserjian RP, Arango Ossa JE et al. **Molecular International Prognostic Scoring System for Myelodysplastic Syndromes**, *NEJM Evidence* 2022.](https://evidence.nejm.org/doi/full/10.1056/EVIDoa2200008) ## :rocket: Installation instructions ```bash # 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. ```js 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) ``` ```js // 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: ```js 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. ```js // 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. ```js import { annotateFile } from 'ipssm' const inputFile = './test/data/IPSSMexample.csv' const outputFile = 'IPSSMexample.annotated.csv' await annotateFile(inputFile, outputFile) ``` or with an excel file: ```js 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. ```text $ 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 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](https://github.com/papaemmelab/ipssm-js/issues) to this repo or to contact [ElsaB](https://elsab.github.io/).