A Clinical Database-Driven Approach to Decision Support: Predicting Mortality Among Patients with Acute Kidney Injury.
A Clinical Database-Driven Approach to Decision Support: Predicting Mortality Among Patients with Acute Kidney Injury.
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DOI：
10.1260/2040-2295.2.1.97
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发表时间：
2011-03
影响因子：
--
通讯作者：
Chueh HC
中科院分区：
文献类型：
--
作者：
Celi LA;Tang RJ;Villarroel MC;Davidzon GA;Lester WT;Chueh HC
为了探索一种基于临床数据库提取信息的决策支持方法，我们建立了急性肾损伤（AKI）重症监护病房（ICU）患者的死亡率预测模型，并将其与简化急性生理评分（SAPS）进行比较。我们使用MIMIC，这是贝斯以色列女执事医疗中心（Beth Israel Deaconess Medical Center） ICU患者的公开去识别数据库，并确定了1400名ICD9诊断为AKI且在ICU住院超过30天的患者。采用入院前72小时的SAPS变量建立多元回归模型。在未见过的测试集上，在训练集上开发的所有模型都优于SAPS (AUC = 0.64, Hosmer-Lemeshow p < 0.001)；最佳模型的AUC = 0.74, Hosmer-Lemeshow p = 0.53。这些发现表明，局部定制模型可能提供更准确的预测。这可能是第一步设想个性化护理点概率模型使用一个人的临床数据库。
In exploring an approach to decision support based on information extracted from a clinical database, we developed mortality prediction models of intensive care unit (ICU) patients who had acute kidney injury (AKI) and compared them against the Simplified Acute Physiology Score (SAPS). We used MIMIC, a public de-identified database of ICU patients admitted to Beth Israel Deaconess Medical Center, and identified 1400 patients with an ICD9 diagnosis of AKI and who had an ICU stay > 3 days. Multivariate regression models were built using the SAPS variables from the first 72 hours of ICU admission. All the models developed on the training set performed better than SAPS (AUC = 0.64, Hosmer-Lemeshow p < 0.001) on an unseen test set; the best model had an AUC = 0.74 and Hosmer-Lemeshow p = 0.53. These findings suggest that local customized modeling might provide more accurate predictions. This could be the first step towards an envisioned individualized point-of-care probabilistic modeling using one’s clinical database.
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急性肾脏损伤的步枪标准与重症患者的医院死亡率有关：一项队列分析。
影响因子：
15.1
作者：
Hoste, Eric A. J.;Clermont, Gilles;Kersten, Alexander;Venkataraman, Ramesh;Angus, Derek C.;De Bacquer, Dirk;Kellum, John A.
通讯作者：
Kellum, John A.
影响因子：
13.2
作者：
Abosaif, NY;Tolba, YA;El Nahas, AM
通讯作者：
El Nahas, AM
影响因子：
120.7
作者：
Uchino, S;Kellum, JA;Ronco, C
通讯作者：
Ronco, C
影响因子：
120.7
作者：
LEGALL, JR;LEMESHOW, S;SAULNIER, F
通讯作者：
SAULNIER, F
影响因子：
6.1
作者：
Li Wen-xiong;Chen Hui-de;Song Yang
通讯作者：
Song Yang