Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Editorial
. 2017 Nov;5(21):436.
doi: 10.21037/atm.2017.08.22.

Development of scoring system for risk stratification in clinical medicine: a step-by-step tutorial

Zhongheng Zhang  1 , Haoyang Zhang  2 , Mahesh Kumar Khanal  3
Affiliations
Editorial

Development of scoring system for risk stratification in clinical medicine: a step-by-step tutorial

Zhongheng Zhang et al. Ann Transl Med. 2017 Nov.

Abstract

Risk scores play an important role in clinical medicine. With advances in information technology and availability of electronic healthcare record, scoring systems of less commonly seen diseases and population can be developed. The aim of the article is to provide a tutorial on how to develop and validate risk scores based on a virtual dataset by using R software. The dataset we generated including numeric and categorical variables and firstly the numeric variables would be converted to factor variables according to cutoff points identified by the LOESS smoother. Then risk points of each variable, which are related to the coefficients in logistic regression, are assigned to each level of the converted factor variables and other categorical variables. Finally, the total score is calculated for each subject to represent the prediction of the outcome event probability. The original dataset is split into training and validation subsets. Discrimination and calibration are evaluated in the validation subset. R codes with explanations are presented in the main text.

Keywords: LOESS smoothing; Scoring system; risk stratification.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
LOESS smoothing curve plotting the probability of death against age. Note the age is not linearly associated with the probability and we need to identify cut points at which y value changes the most.
Figure 2
Figure 2
LOESS smoothing curve plotting the probability of death against lac.
Figure 3
Figure 3
Predicted probability of death versus the number of observed survivors and non-survivors.
Figure 4
Figure 4
The predicted probability is plotted against the observed probability. A variety of statistics are displayed on the top left.
See this image and copyright information in PMC

References

    1. Anderson KM, Odell PM, Wilson PW, et al. Cardiovascular disease risk profiles. Am Heart J 1991;121:293-8. 10.1016/0002-8703(91)90861-B - DOI - PubMed
    1. Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ 2008;336:1475-82. 10.1136/bmj.39609.449676.25 - DOI - PMC - PubMed
    1. Le Gall JR, Lemeshow S, Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA 1993;270:2957-63. 10.1001/jama.1993.03510240069035 - DOI - PubMed
    1. Minne L, Abu-Hanna A, de Jonge E. Evaluation of SOFA-based models for predicting mortality in the ICU: A systematic review. Crit Care 2008;12:R161. 10.1186/cc7160 - DOI - PMC - PubMed
    1. Le Gall JR, Klar J, Lemeshow S, et al. The Logistic Organ Dysfunction system. A new way to assess organ dysfunction in the intensive care unit. ICU Scoring Group. JAMA 1996;276:802-10. 10.1001/jama.1996.03540100046027 - DOI - PubMed

Publication types

LinkOut - more resources