Developing practical clinical tools for predicting neonatal mortality at a neonatal intensive care unit in Tanzania.
Developing practical clinical tools for predicting neonatal mortality at a neonatal intensive care unit in Tanzania.
复制标题
DOI：
10.1186/s12887-021-03012-4
复制
发表时间：
2021-12-01
期刊：
影响因子：
2.4
通讯作者：
Matthews L
中科院分区：
文献类型：
--
作者：
Kovacs D;Msanga DR;Mshana SE;Bilal M;Oravcova K;Matthews L
坦桑尼亚的新生儿死亡率仍然很高，每 1000 名活产婴儿中约有 20 人死亡。低出生体重、早产和窒息与新生儿死亡率有关；然而，尚无研究评估结合基础疾病和生命体征为临床医生提供死亡风险婴儿早期预警的价值。本研究的目的是确定与坦桑尼亚姆万扎布干多医疗中心 (BMC) 新生儿重症监护病房 (NICU) 新生儿死亡率相关的危险因素（包括生命体征）；确定用于预测死亡率的最准确的广义线性模型 (GLM) 或决策树；并提供一种工具，提供临床相关的截止值来预测死亡率，以便临床医生在资源匮乏的情况下轻松使用。 2019年11月至2020年3月期间，共有165名新生儿入组，其中80名（48.5%）死亡。我们通过对数据重新采样以创建训练和测试数据集并比较它们正确预测死亡率的准确性来竞争 GLM 和决策树的性能。 GLM 的性能始终优于决策树。最适合的 GLM 显示（对于标准化危险因素）温度（OR 0.61，95% CI 0.40–0.90）、出生体重（OR 0.33，95% CI 0.20–0.52）和氧饱和度（OR 0.66，95% CI 0.45–0.94）与死亡率呈负相关，而心率（OR 1.59，95% CI 0.45–0.94）与死亡率呈负相关。 1.10–2.35）和 窒息（OR 3.23，95% 1.25–8.91）是危险因素。为了确定在资源匮乏的临床环境中平衡准确性和易用性的工具，我们将最合适的 GLM 与更简单的版本进行了比较，并确定了包含温度、心率和出生体重的三变量 GLM 作为最佳候选。对于该工具，使用受试者工作特征 (ROC) 曲线确定截止值，死亡率预测的最佳截止值对应于 76.3% 的敏感性和 68.2% 的特异性。最后一个工具是图形化的，显示了取决于出生体重、心率和体温的截止值。基础状况和生命体征可以组合成简单的图形工具，这些工具改进了当前的指南，并且在资源匮乏的情况下可供临床医生直接使用。
Neonatal mortality remains high in Tanzania at approximately 20 deaths per 1000 live births. Low birthweight, prematurity, and asphyxia are associated with neonatal mortality; however, no studies have assessed the value of combining underlying conditions and vital signs to provide clinicians with early warning of infants at risk of mortality. The aim of this study was to identify risk factors (including vital signs) associated with neonatal mortality in the neonatal intensive care unit (NICU) in Bugando Medical Centre (BMC), Mwanza, Tanzania; to identify the most accurate generalised linear model (GLM) or decision tree for predicting mortality; and to provide a tool that provides clinically relevant cut-offs for predicting mortality that is easily used by clinicians in a low-resource setting. In total, 165 neonates were enrolled between November 2019 and March 2020, of whom 80 (48.5%) died. We competed the performance of GLMs and decision trees by resampling the data to create training and test datasets and comparing their accuracy at correctly predicting mortality. GLMs always outperformed decision trees. The best fitting GLM showed that (for standardised risk factors) temperature (OR 0.61, 95% CI 0.40–0.90), birthweight (OR 0.33, 95% CI 0.20–0.52), and oxygen saturation (OR 0.66, 95% CI 0.45–0.94) were negatively associated with mortality, while heart rate (OR 1.59, 95% CI 1.10–2.35) and asphyxia (OR 3.23, 95% 1.25–8.91) were risk factors. To identify the tool that balances accuracy and with ease of use in a low-resource clinical setting, we compared the best fitting GLM with simpler versions, and identified the three-variable GLM with temperature, heart rate, and birth weight as the best candidate. For this tool, cut-offs were identified using receiver operator characteristic (ROC) curves with the optimal cut-off for mortality prediction corresponding to 76.3% sensitivity and 68.2% specificity. The final tool is graphical, showing cut-offs that depend on birthweight, heart rate, and temperature. Underlying conditions and vital signs can be combined into simple graphical tools that improve upon the current guidelines and are straightforward to use by clinicians in a low-resource setting.
登录
查看更多内容
影响因子：
--
作者：
Oonsivilai, Mathupanee;Mo, Yin;Cooper, Ben S
通讯作者：
Cooper, Ben S
坦桑尼亚北部新生儿护理病房的特定原因新生儿死亡率：一项基于登记的队列研究。
影响因子：
2.4
作者：
Mmbaga BT;Lie RT;Olomi R;Mahande MJ;Kvåle G;Daltveit AK
通讯作者：
Daltveit AK
影响因子：
2.8
作者：
Surucu, Murat;Shah, Karan K.;Emami, Bahman
通讯作者：
Emami, Bahman
影响因子：
34.3
作者：
Afnan-Holmes, Hoviyeh;Magoma, Moke;Lawn, Joy E.
通讯作者：
Lawn, Joy E.
影响因子：
3.7
作者：
Malhotra A;Rachet B;Bonaventure A;Pereira SP;Woods LM
通讯作者：
Woods LM