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南非患者新冠病毒病出院后死亡风险的回顾性评估

Retrospective Post-Hospitalisation COVID-19 Mortality Risk Assessment of Patients in South Africa.

作者信息

Boateng Alexander, Maposa Daniel, Mokobane Reshoketswe

机构信息

Department of Biostatistics, University of the Free State, Bloemfontein 9300, South Africa.

Department of Statistics and Operations Research, University of Limpopo, Polokwane 0727, South Africa.

出版信息

Eur J Investig Health Psychol Educ. 2023 Sep 1;13(9):1655-1675. doi: 10.3390/ejihpe13090120.

DOI:10.3390/ejihpe13090120
PMID:37754459
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10528257/
Abstract

: This study explores the determinants impacting the mortality risk of COVID-19 patients following hospitalisation within South Africa's Limpopo province. : Utilising a dataset comprising 388 patients, the investigation employs a frailty regression model to evaluate the influence of diverse characteristics on mortality outcomes, contrasting its performance against other parametric models based on loglikelihood measures. : The findings underscore diabetes and hypertension as notable contributors to heightened mortality rates, underscoring the urgency of effectively managing these comorbidities to optimise patient well-being. Additionally, regional discrepancies come to the fore, with the Capricorn district demonstrating elevated mortality risks, thereby accentuating the necessity for precisely targeted interventions. Medical interventions, particularly ventilation, emerge as pivotal factors in mitigating mortality risk. Gender-based distinctions in mortality patterns also underscore the need for bespoke patient care strategies. : Collectively, these outcomes supply practical insights with implications for healthcare interventions, policy formulation, and clinical strategies aimed at ameliorating COVID-19 mortality risk among individuals discharged from hospitals within South Africa's Limpopo province.

摘要

本研究探讨了影响南非林波波省住院后新冠病毒肺炎患者死亡风险的决定因素。利用一个包含388名患者的数据集,该调查采用脆弱性回归模型来评估各种特征对死亡结果的影响,并根据对数似然度量将其性能与其他参数模型进行对比。研究结果强调糖尿病和高血压是死亡率升高的显著因素,凸显了有效管理这些合并症以优化患者健康的紧迫性。此外,地区差异凸显出来,摩羯座地区显示出较高的死亡风险,从而突出了精准靶向干预措施的必要性。医疗干预措施,尤其是通气,是降低死亡风险的关键因素。死亡模式中的性别差异也凸显了定制患者护理策略的必要性。总体而言,这些结果提供了实用见解,对旨在降低南非林波波省出院患者新冠病毒肺炎死亡风险的医疗干预措施、政策制定和临床策略具有启示意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dc3/10528257/65d00900a138/ejihpe-13-00120-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dc3/10528257/bc6ec3b4beeb/ejihpe-13-00120-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dc3/10528257/ac05972fc7a2/ejihpe-13-00120-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dc3/10528257/92f7c118b40a/ejihpe-13-00120-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dc3/10528257/65d00900a138/ejihpe-13-00120-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dc3/10528257/bc6ec3b4beeb/ejihpe-13-00120-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dc3/10528257/ac05972fc7a2/ejihpe-13-00120-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dc3/10528257/92f7c118b40a/ejihpe-13-00120-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dc3/10528257/65d00900a138/ejihpe-13-00120-g003.jpg

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本文引用的文献

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Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay.用于住院 COVID-19 死亡率评估的可解释机器学习:糖尿病的独特作用。
Sensors (Basel). 2022 Nov 12;22(22):8757. doi: 10.3390/s22228757.
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Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data.利用机器学习和现成的临床数据预测 COVID-19 患者的预后。
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Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements.
通过对纵向测量数据的机器学习发现的11种常规临床特征可预测新冠病毒疾病(COVID-19)的严重程度。
Comput Struct Biotechnol J. 2021;19:3640-3649. doi: 10.1016/j.csbj.2021.06.022. Epub 2021 Jun 17.
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A predictive model of clinical deterioration among hospitalized COVID-19 patients by harnessing hospital course trajectories.利用住院期间的病程轨迹预测 COVID-19 住院患者临床恶化的模型。
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Health-related quality of life of COVID-19 patients after discharge: A multicenter follow-up study.新冠病毒感染康复者出院后的健康相关生活质量:一项多中心随访研究。
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Management of STEMI during the COVID-19 pandemic: Lessons learned in 2020 to prepare for 2021.COVID-19 大流行期间的 STEMI 管理:2020 年汲取的经验教训,为 2021 年做准备。
Trends Cardiovasc Med. 2021 Apr;31(3):135-140. doi: 10.1016/j.tcm.2020.12.003. Epub 2020 Dec 15.
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COVID-19 and the gender health paradox.COVID-19 与性别健康悖论。
Scand J Public Health. 2021 Feb;49(1):17-26. doi: 10.1177/1403494820975604. Epub 2020 Dec 14.
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Racial Disparities in Incidence and Outcomes Among Patients With COVID-19.COVID-19 患者的发病和结局中的种族差异。
JAMA Netw Open. 2020 Sep 1;3(9):e2021892. doi: 10.1001/jamanetworkopen.2020.21892.
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