Prediction modelling of inpatient neonatal mortality in high-mortality settings.
Prediction modelling of inpatient neonatal mortality in high-mortality settings.
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DOI：
10.1136/archdischild-2020-319217
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发表时间：
2021-04-21
影响因子：
5.2
通讯作者：
English M
中科院分区：
文献类型：
--
作者：
Aluvaala J;Collins G;Maina B;Mutinda C;Waiyego M;Berkley JA;English M
预后模型有助于临床决策和医院绩效评估。现有的新生儿预后模型通常使用在资源匮乏地区的常规实践中通常无法获得的生理测量值，例如脉搏血氧饱和度值。我们的目的是开发和验证两种新模型，以预测资源匮乏、死亡率高的环境下新生儿入院后的全因院内死亡率。我们使用值班临床医生在入院时记录的基本常规临床数据来推导 (n=5427) 并验证 (n=1627) 两个预测院内死亡率的新模型。新生儿基本治疗评分 (NETS) 包括入院时规定的治疗，而新生儿基本症状和体征评分 (SENSS) 使用基本临床体征。使用逻辑回归，并使用辨别和校准来评估性能。推导时，NETS 的 c 统计量（歧视）为 0.92（95% CI 0.90 至 0.93），SENSS 的 c 统计量（歧视）为 0.91（95% CI 0.89 至 0.93）。在外部（时间）验证中，NETS 的 c 统计值为 0.89（95% CI 0.86 至 0.92），SENSS 为 0.89（95% CI 0.84 至 0.93）。 NETS 的校准截距为 -0.72（95% CI -0.96 至 -0.49），SENSS 的校准截距为 -0.33（95% CI -0.56 至 -0.11）。使用资源匮乏环境中的常规新生儿数据，我们发现可以通过治疗或体征和症状来预测院内死亡率。这些模型的进一步验证可能支持它们在治疗决策和病例组合调整中的使用，以帮助了解不同医院的绩效差异。
Prognostic models aid clinical decision making and evaluation of hospital performance. Existing neonatal prognostic models typically use physiological measures that are often not available, such as pulse oximetry values, in routine practice in low-resource settings. We aimed to develop and validate two novel models to predict all cause in-hospital mortality following neonatal unit admission in a low-resource, high-mortality setting. We used basic, routine clinical data recorded by duty clinicians at the time of admission to derive (n=5427) and validate (n=1627) two novel models to predict in-hospital mortality. The Neonatal Essential Treatment Score (NETS) included treatments prescribed at the time of admission while the Score for Essential Neonatal Symptoms and Signs (SENSS) used basic clinical signs. Logistic regression was used, and performance was evaluated using discrimination and calibration. At derivation, c-statistic (discrimination) for NETS was 0.92 (95% CI 0.90 to 0.93) and that for SENSS was 0.91 (95% CI 0.89 to 0.93). At external (temporal) validation, NETS had a c-statistic of 0.89 (95% CI 0.86 to 0.92) and SENSS 0.89 (95% CI 0.84 to 0.93). The calibration intercept for NETS was −0.72 (95% CI −0.96 to −0.49) and that for SENSS was −0.33 (95% CI −0.56 to −0.11). Using routine neonatal data in a low-resource setting, we found that it is possible to predict in-hospital mortality using either treatments or signs and symptoms. Further validation of these models may support their use in treatment decisions and for case-mix adjustment to help understand performance variation across hospitals.
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影响因子：
3.7
作者：
Aluvaala J;Collins GS;Maina M;Berkley JA;English M
通讯作者：
English M
影响因子：
34.3
作者：
Hug, Lucia;Alexander, Monica;Wakefield, John
通讯作者：
Wakefield, John
影响因子：
2.6
作者：
Kihuba, Elesban;Gathara, David;English, Mike
通讯作者：
English, Mike
影响因子：
5.2
作者：
Aluvaala, Jalemba;Nyamai, Rachael;English, Mike
通讯作者：
English, Mike
影响因子：
4
作者：
English M;Ayieko P;Nyamai R;Were F;Githanga D;Irimu G
通讯作者：
Irimu G