Nursing Division of the Department of Neurology, Nanjing Drum Tower Hospital Affiliated to Nanjing University Medical School, Nanjing, China.
Nurs Open. 2021 May;8(3):1380-1392. doi: 10.1002/nop2.754. Epub 2020 Dec 30.
To develop and internally validate a nomogram to predict the risk of death within 6 months of onset of stroke in Chinese. Identifying risk factors with potentially direct effects on the nomogram will improve the quality of risk assessment and help nurses implement preventive measures based on patient-specific risk factors.
A retrospective study.
We performed a least absolute shrinkage and selection operator (LASSO) regression modelling and multivariate logistic regression analysis to establish a prediction model of death risk in stroke patients within 6 months of onset. LASSO and time-dependent Cox regression models were further used to analyse the 6-month survival of stroke patients. Data were collected from 21 October 2013-6 May 2019.
The independent predictors of the nomogram were Barthel index (odds ratio (OR) = 0.980, 95% confidence interval (CI) = 0.961-0.998, p = .03), platelet/lymphocyte ratio (OR = 1.005, 95% CI = 1.000-1.010, p = .04) and serum albumin (OR = 0.854, 95% CI = 0.774-0.931, p < .01). This model showed good discrimination and consistency, and its discrimination evaluation C-statistic was 0.879 in the training set and 0.891 in the internal validation set. The DCA indicated that the nomogram had a higher overall net benefit over most of the threshold probability range. The time-dependent Cox regression model established the impact of the time effect of the age variable on survival time.
Our results identified three predictors of death within 6 months of stroke in Chinese. These predictors can be used as risk assessment indicators to help caregivers performing clinical nursing work, and in clinical practice, it is suggested that nurses should evaluate the self-care ability of stroke patients in detail. The constructed nomogram can help identify patients at high risk of death within 6 months, so that intervention can be performed as early as possible.
开发并内部验证一个列线图，以预测中国人群中风发病后 6 个月内的死亡风险。确定对列线图有潜在直接影响的风险因素将提高风险评估的质量，并有助于护士根据患者特定的风险因素实施预防措施。
回顾性研究。
我们进行了最小绝对收缩和选择算子（LASSO）回归建模和多变量逻辑回归分析，以建立中风发病后 6 个月内死亡风险的预测模型。LASSO 和时依 Cox 回归模型进一步用于分析中风患者的 6 个月生存率。数据于 2013 年 10 月 21 日至 2019 年 5 月 6 日采集。
该列线图的独立预测因子为巴氏指数（比值比（OR）=0.980，95%置信区间（CI）=0.961-0.998，p=0.03）、血小板/淋巴细胞比值（OR=1.005，95%CI=1.000-1.010，p=0.04）和血清白蛋白（OR=0.854，95%CI=0.774-0.931，p<0.01）。该模型具有良好的区分度和一致性，在训练集中的判别评估 C 统计量为 0.879，在内部验证集中为 0.891。DCA 表明，该列线图在大多数阈值概率范围内具有更高的总体净效益。时依 Cox 回归模型建立了年龄变量时间效应对生存时间的影响。
本研究在中国人群中确定了中风发病后 6 个月内死亡的三个预测因子。这些预测因子可作为风险评估指标，帮助护理人员开展临床护理工作，在临床实践中，建议护士详细评估中风患者的自理能力。构建的列线图可以帮助识别 6 个月内死亡风险较高的患者，以便尽早进行干预。
