Development of a Nomogram model for early diagnosis of Parkinson disease

doi:10.16150/j.1671-2870.2023.03.11

Abstract

Abstract:

Objective: To construct a Nomogram model for early diagnosis of Parkinson’s and validate it. Methods: This study consecutively enrolled 201 Parkinson’s patients who were hospitalized in Ruijin Hospital affiliated to Shanghai Jiaotong University between June 2013 and December 2019 as the Parkinson’s group; 201 Patients with chronic neurological diseases of non-primary Parkinson’s disease who were hospitalized during the same period served as the control group. Of the 402 cases, 300 cases (150 Parkinson’s patients and 150 non-Parkinson’s patients) were stratified and randomly selected as the training set. The remaining 102 cases (51 Parkinson’s patients and 51 non-Parkinson’s patients) were used as the validation set. In this study, R software (version 4.2.1) with complete data processing, computation, and graphing capabilities was used to analyze the data in the training set. Univariate logistic regression analysis was used to screen the risk factors for Parkinson’s disease, and multivariate logistic regression analysis and nomogram model construction were further performed. Calibration and ROC curves were used to perform internal and external validation on the training and validation sets respectively. Results: Multivariate Logistic regression analysis showed advanced age（>60） (OR=3.987 95%CI=2.126-7.477 P=0.131), cognitive dysfunction（MoCA score >26） (OR=3.094 95%CI=1.654-5.787 P<0.001), constipation (OR=2.630 95%CI=1.430-4.835 P=0.002), RBD (OR=2.710 95%CI=1.449-5.068 P=0.002), hyposmia (OR=2.117 95%CI=1.172-3.824 P=0.013), and reduced CER（<20 mg/L） (OR=3.356 95%CI=1.923-5.855 P<0.001) were risk factors for Parkinson’s. The Nomogram model was established based on the above risk factors, and the areas under the internal and external validation curves were 0.729 and 0.714, respectively. Conclusions: The Nomogram diagnostic model can effectively assist in diagnosing Parkinson’s disease and has certain clinical application value.

Key words: Parkinson’s disease, Copper blue protein, Diagnostic model, Nomogram.

CLC Number:

R554.1

WEI Jian, SUN Jie, CUI Shishuang. Development of a Nomogram model for early diagnosis of Parkinson disease[J]. Journal of Diagnostics Concepts & Practice, 2023, 22(03): 277-282.

Figures/Tables 6

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References 29

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Characteristics	Training set （n=300）	Validation set （n=102）	P value
Gender			0.633
Female	167 (41.5%)	54 (13.4%)
Male	133 (33.1%)	48 (11.9%)
Agedness			0.278
Yes	219 (54.5%)	80 (19.9%)
No	81 (20.1%)	22 (5.5%)
diabetes			0.310
Yes	38 (9.5%)	17 (4.2%)
No	262 (65.2%)	85 (21.1%)
Cognitive decline			0.830
Yes	218 (54.2%)	73 (18.2%)
No	82 (20.4%)	29 (7.2%)
RBD			0.840
Yes	212 (52.7%)	71 (17.7%)
No	88 (21.9%)	31 (7.7%)
Constipation			0.814
Yes	214 (53.2%)	74 (18.4%)
No	86 (21.4%)	28 (7%)
Hyposmia			0.136
Yes	212 (52.7%)	64 (15.9%)
No	88 (21.9%)	38 (9.5%)
CER	195.95 (±15.15)	202 (±51.42)	0.109
Hypertension			0.440
Yes	38 (9.5%)	16 (4%)
No	262 (65.2%)	86 (21.4%)
Postural hypotension			0.542
Yes	238 (59.2%)	78 (19.4%)
No	62 (15.4%)	24 (6%)
Smoking			0.570
Yes	73 (18.2%)	22 (5.5%)
No	227 (56.5%)	80 (19.9%)
Drinking			0.333
Yes	76 (18.9%)	21 (5.2%)
No	224 (55.7%)	81 (20.1%)

Characteristics	OR	95% CI	P value
Gender	0.703	0.445-1.111	0.131
Agedness	2.979	1.732-5.122	<0.001
Diabetes	1.274	0.643-2.523	0.488
Cognitive decline	2.280	1.348-3.856	0.002
RBD	1.919	1.156-3.187	0.012
Constipation	2.383	1.418-4.005	<0.001
Hyposmia	1.795	1.083-2.975	0.023
CER	2.389	1.477-3.866	<0.001
Hypertension	0.785	0.396-1.556	0.488
Postural hypotension	1.562	0.890-2.741	0.120
Smoking	1.296	0.759-2.211	0.342
Drinking	0.732	0.422-1.268	0.266

Characteristics	regression coefficient	OR	OR(95% CI)	P value
Agedness	1.228	3.413	1.884 - 6.182	<0.001
Cognitive decline	0.951	2.588	1.444 - 4.642	<0.001
RBD	0.665	1.945	1.090- 3.471	0.024
Constipation	1.064	2.897	1.623 - 5.169	<0.001
Hyposmia	0.751	2.120	1.208 - 3.722	0.009
CER	1.211	3.356	1.923 - 5.855	<0.001