Department of Radiology, Charlotte Maxeke Johannesburg Academic Hospital, and Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Department of Internal Medicine, Charlotte Maxeke Johannesburg Academic Hospital, and Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Department of Respiratory Medicine, Waikato District Health Board, Hamilton, New Zealand.
S Afr Med J. 2023 Feb 1;113(2):75-83. doi: 10.7196/SAMJ.2023.v113i2.16681.
Chest radiographic scoring systems for COVID-19 pneumonia have been developed. However, little is published on the utilityof these scoring systems in low- and middle-income countries.
To perform risk stratification of COVID-19 pneumonia in Johannesburg, South Africa (SA), by comparing the Brixia score withclinical parameters, disease course and clinical outcomes. To assess inter-rater reliability and developing predictive models of the clinicaloutcome using the Brixia score and clinical parameters.
Retrospective investigation was conducted of adult participants with established COVID-19 pneumonia admitted at a tertiaryinstitution from 1 May to 30 June 2020. Two radiologists, blinded to clinical data, assigned Brixia scores. Brixia scores were compared withclinical parameters, length of stay and clinical outcomes (discharge/death). Inter-rater agreement was determined. Multivariable logisticregression extracted variables predictive of in-hospital demise.
The cohort consisted of 263 patients, 51% male, with a median age of 47 years (interquartile range (IQR) = 20; 95% confidenceinterval (CI) 46.5 - 49.9). Hypertension (38.4%), diabetes (25.1%), obesity (19.4%) and HIV (15.6%) were the most common comorbidities.The median length of stay for 258 patients was 7.5 days (IQR = 7; 95% CI 8.2 - 9.7) and 6.5 days (IQR = 8; 95% CI 6.5 - 12.5) for intensivecare unit stay. Fifty (19%) patients died, with a median age of 55 years (IQR = 23; 95% CI 50.5 - 58.7) compared with survivors, of medianage 46 years (IQR = 20; 95% CI 45 - 48.6) (p=0.01). The presence of one or more comorbidities resulted in a higher death rate (23% v. 9.2%;p=0.01) than without comorbidities. The median Brixia score for the deceased was higher (14.5) than for the discharged patients (9.0)(p<0.001). Inter-rater agreement for Brixia scores was good (intraclass correlation coefficient 0.77; 95% CI 0.6 - 0.85; p<0.001). A modelcombining Brixia score, age, male gender and obesity (sensitivity 84%; specificity 63%) as well as a model with Brixia score and C-reactiveprotein (CRP) count (sensitivity 81%; specificity 63%) conferred the highest risk for in-hospital mortality.
We have demonstrated the utility of the Brixia scoring system in a middle-income country setting and developed the first SArisk stratification models incorporating comorbidities and a serological marker. When used in conjunction with age, male gender, obesityand CRP, the Brixia scoring system is a promising and reliable risk stratification tool. This may help inform the clinical decision pathway inresource-limited settings like ours during future waves of COVID-19.
已经开发出用于 COVID-19 肺炎的胸部放射学评分系统。然而，在中低收入国家，关于这些评分系统的实用性的研究却很少。
通过比较 Brixia 评分与临床参数、疾病过程和临床结局，对南非约翰内斯堡的 COVID-19 肺炎进行风险分层。评估 Brixia 评分和临床参数对临床结局的预测能力，并建立预测模型。
对 2020 年 5 月 1 日至 6 月 30 日期间在一家三级机构住院的确诊 COVID-19 肺炎的成年患者进行回顾性研究。两位放射科医生在不了解临床数据的情况下对 Brixia 评分进行了评估。Brixia 评分与临床参数、住院时间和临床结局（出院/死亡）进行了比较。确定了两位放射科医生之间的一致性。多变量逻辑回归提取了预测住院死亡的变量。
该队列包括 263 名患者，51%为男性，中位年龄为 47 岁（四分位间距（IQR）= 20；95%置信区间（CI）为 46.5-49.9）。最常见的合并症为高血压（38.4%）、糖尿病（25.1%）、肥胖症（19.4%）和 HIV（15.6%）。258 名患者的中位住院时间为 7.5 天（IQR = 7；95%CI 8.2-9.7），25 名患者入住重症监护病房的中位时间为 6.5 天（IQR = 8；95%CI 6.5-12.5）。50 名（19%）患者死亡，中位年龄为 55 岁（IQR = 23；95%CI 50.5-58.7），与幸存者相比，中位年龄为 46 岁（IQR = 20；95%CI 45-48.6）（p=0.01）。存在一种或多种合并症的患者死亡率（23%比 9.2%；p=0.01）高于无合并症的患者。死亡患者的 Brixia 评分中位数（14.5）高于出院患者（9.0）（p<0.001）。Brixia 评分的两位放射科医生之间的一致性良好（组内相关系数 0.77；95%CI 0.6-0.85；p<0.001）。一个将 Brixia 评分、年龄、男性和肥胖症相结合的模型（敏感性 84%；特异性 63%）以及一个将 Brixia 评分和 C 反应蛋白（CRP）计数相结合的模型（敏感性 81%；特异性 63%）可以最高程度地预测住院死亡率。
我们已经证明了 Brixia 评分系统在中等收入国家环境中的实用性，并开发了第一个结合合并症和血清学标志物的 SAR 风险分层模型。当与年龄、男性、肥胖症和 CRP 结合使用时，Brixia 评分系统是一种有前途且可靠的风险分层工具。这可能有助于在我们这样的资源有限的环境中为未来的 COVID-19 浪潮提供临床决策路径。
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