埃塞俄比亚Bahir Bar的Felege Hiwot转诊医院青光眼患者眼压和致盲时间的决定因素:独立模型和联合模型的比较

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2021-09-18 eCollection Date: 2021-01-01 DOI:10.1177/11769351211045975
Mitiku Wale Muluneh, Awoke Seyoum Tegegne
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引用次数: 1

摘要

背景:由于青光眼病例数量在未来几十年内大幅增加,青光眼是一个重大的公共卫生问题。本研究的主要目的是调查在埃塞俄比亚Bahir Dar的Felege Hiwot转诊医院接受治疗的青光眼患者眼压和致盲时间的决定因素。方法:采用简单随机抽样的方法,根据2014年1月至2018年12月在该院眼科门诊就诊的患者识别号,随机抽取328例青光眼患者进行回顾性研究设计。眼压数据采用线性混合效应模型,失明时间数据采用半参数生存模型,两种反应联合建模进行数据分析。然而,主要结局是青光眼患者的生存时间。结果:联合模型与独立模型的比较表明,联合模型在参数估计上的标准误差较小,是更充分、更有效的推断。这也被批准使用AIC, BIC,并基于显著似然比检验。联合模型估计的关联参数(α)为0.0160,具有统计学意义(p值= 0.049)。这表明有强有力的证据表明眼压的影响与失明的风险之间存在正相关。结果表明,眼压值越高,致盲风险越高。年龄、高血压、用药类型、杯盘比对青光眼患者的平均眼压和生存时间均有显著影响(p值)。年龄、高血压、用药类型、杯盘比与青光眼患者的两种反应有显著相关。医护人员在随访期间对血压和杯盘比大于0.7的患者给予更多的关注,以降低青光眼患者失明的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Determinants of Intraocular Pressure and Time to Blindness for Glaucoma Patients at Felege Hiwot Referral Hospital, Bahir Bar, Ethiopia: A Comparison of Separate and Joint Models.

Determinants of Intraocular Pressure and Time to Blindness for Glaucoma Patients at Felege Hiwot Referral Hospital, Bahir Bar, Ethiopia: A Comparison of Separate and Joint Models.

Determinants of Intraocular Pressure and Time to Blindness for Glaucoma Patients at Felege Hiwot Referral Hospital, Bahir Bar, Ethiopia: A Comparison of Separate and Joint Models.

Determinants of Intraocular Pressure and Time to Blindness for Glaucoma Patients at Felege Hiwot Referral Hospital, Bahir Bar, Ethiopia: A Comparison of Separate and Joint Models.

Background: Due to the substantial increase in the number of glaucoma cases within the next several decades, glaucoma is a significant public health issue. The main objective of this study was to investigate the determinant factors of intraocular pressure and time to blindness of glaucoma patients under treatment at Felege Hiwot Referral Hospital, Bahir Dar, Ethiopia.

Methods: A retrospective study design was conducted on 328 randomly selected glaucoma patients using simple random sampling based on the identification number of patients in an ophthalmology clinic at the hospital under the follow-up period from January 2014 to December 2018. A linear mixed effects model for intraocular pressure data, a semi-parametric survival model for the time-to-blindness data and joint modeling of the 2 responses were used for data analysis. However, the primary outcome was survival time of glaucoma patients.

Results: The comparison of joint and separate models revealed that joint model was more adequate and efficient inferences because of its smaller standard errors in parameter estimations. This was also approved using AIC, BIC, and based on a significant likelihood ratio test as well. The estimated association parameter (α) in the joint model was .0160 and statistically significant (P-value = .0349). This indicated that there was strong evidence for positive association between the effects of intraocular pressure and the risk of blindness. The result indicated that the higher value of intraocular pressure was associated with the higher risk of blindness. Age, hypertension, type of medication, cup-disk ratio significantly affects both average intraocular pressure and survival time of glaucoma patients (P-value < .05).

Conclusion: The predictors; age, hypertension, type of medication, and cup-disk ratio were significantly associated with the 2 responses of glaucoma patients. Health professionals give more attention to patients who have blood pressure and cup-disk ratio greater than 0.7 during the follow-up time to reduce the risk of blindness of glaucoma patients.

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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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