A nomogram to predict long COVID risk based on pre- and post-infection factors: Results from a cross-sectional study in South China

IF 3.9 3区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Public Health Pub Date : 2024-10-17 DOI:10.1016/j.puhe.2024.09.023

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引用次数: 0

Abstract

Objectives

Long COVID has received much attention as a complex multi-system disease due to its serious impact on quality of life. However, there remains inconsistent results in terms of risk factors, and a prediction model for the accurate prediction of long COVID is still lacking.

Study design

Cross-sectional study.

Methods

In this retrospective study, a community population from the Futian District of Shenzhen, Guangdong Province, China, were included. Data were collected from September to December 2023 using an electronic questionnaire. Logistic regression analyses were used to identify predictors of long COVID. Pre-infection and post-infection prediction models (with/without post-infection characteristics) were developed, and the C-index was used to evaluate accuracy.

Results

In total, 420 patients infected COVID-19 were included. The prevalence of long COVID was 32.9 %. The most common symptoms of long COVID were weakness/fatigue, persistent cough and cognitive dysfunction. Independent predictors of long COVID included in the pre-infection model were age, long-term medication, and psychological problems such as stress and doing things without enthusiasm/interest before COVID-19 infection (C-index: 0.721). Independent predictors included in the post-infection model were age, inability to concentrate before COVID-19 infection, and symptoms of weakness/fatigue, abnormal smell/taste, diarrhoea, eye conjunctivitis and headache/dizziness during the acute-phase (C-index: 0.857).

Conclusions

Age, psychological problems before COVID-19 infection and acute-phase symptoms were important risk factors of long COVID. Results from the pre-infection model provide guidance for non-infected individuals on how to prevent long COVID. Results from the post-infection model can be used to accurately predict individuals who are at high risk of long COVID and help design treatment plans for patients in the acute phase.

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基于感染前后因素预测长COVID风险的提名图：华南地区横断面研究的结果

研究目的长COVID作为一种复杂的多系统疾病，因其对生活质量的严重影响而备受关注。研究设计横断面研究。方法在这项回顾性研究中，纳入了中国广东省深圳市福田区的社区人群。通过电子问卷收集了 2023 年 9 月至 12 月期间的数据。研究采用逻辑回归分析来确定长COVID的预测因素。结果共纳入420名感染COVID-19的患者。长COVID的发病率为32.9%。长COVID最常见的症状是虚弱/疲劳、持续咳嗽和认知功能障碍。感染前模型中的长COVID独立预测因子包括年龄、长期服药和心理问题，如感染COVID-19前的压力和做事缺乏热情/兴趣（C指数：0.721）。感染后模型中的独立预测因素包括年龄、感染 COVID-19 前无法集中注意力，以及急性期的虚弱/疲劳、嗅觉/味觉异常、腹泻、眼结膜炎和头痛/头晕症状（C 指数：0.857）。感染前模型的结果为非感染者如何预防长COVID提供了指导。感染后模型的结果可用于准确预测长COVID的高危人群，并帮助设计急性期患者的治疗方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Public Health 医学-公共卫生、环境卫生与职业卫生

CiteScore

7.60

自引率

0.00%

发文量

280

审稿时长

37 days

期刊介绍： Public Health is an international, multidisciplinary peer-reviewed journal. It publishes original papers, reviews and short reports on all aspects of the science, philosophy, and practice of public health.