Monitoring Anesthesia Care Delivery and Perioperative Mortality in Kenya Utilizing a Provider-driven Novel Data Collection Tool
Monitoring Anesthesia Care Delivery and Perioperative Mortality in Kenya Utilizing a Provider-driven Novel Data Collection Tool
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DOI：
10.1097/aln.0000000000001713
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发表时间：
2017-08-01
期刊：
影响因子：
8.8
通讯作者：
McEvoy, Matthew D.
中科院分区：
文献类型：
--
作者：
Sileshi, Bantayehu;Newton, Mark W.;McEvoy, Matthew D.
背景：围手术期死亡率被视为围手术期护理的可靠质量和安全指标，但低收入和中等收入国家的记录很少。我们在东非国家开发并测试了一种电子的、提供者报告驱动的方法。方法：我们在肯尼亚三级转诊医院部署了一个数据收集工具，用于收集特定病例的围手术期数据，并异步自动传输到中央服务器。该工具未捕获的案例（未观察到的案例）是在数据收集期的最后两个季度手动收集的。我们创建了逻辑回归模型来分析手术类型对死亡率的影响。结果：2014 年 1 月至 2015 年 9 月期间，捕获了 11,875 例病例中的 8,419 例。上季度季度数据捕获率范围为 423 (26%) 至 1,663 (93%)。 7 天时报告有 93 人死亡（1.53%）。与剖宫产中的 4 例死亡 (0.53%) 相比，普通手术 (n = 42 [3.65%]；优势比 = 15.80 [95% CI，5.20 至 48.10]；P < 0.001)、神经外科 (n = 19 [2.41%]；优势比 = 14.08 [95% CI，4.12 至 48.10]； 48.10]；P < 0.001）和急诊手术（n = 25 [3.63%]；比值比 = 4.40 [95% CI，2.46 至 7.86]；P < 0.001）具有较高的死亡风险。非观察组与电子捕获病例在 7 天死亡率方面没有差异（n = 1 [0.23%] vs. n = 16 [0.58%]；比值比 = 3.95 [95% CI，0.41 至 38.20]；P = 0.24）。 结论：我们创建了一个简单的解决方案，用于在中低收入环境中进行大量、前瞻性的围手术期数据电子收集。我们成功地使用该工具从肯尼亚的一个中心收集了大量病例，并观察了手术类型之间的死亡率差异。
Background: Perioperative mortality rate is regarded as a credible quality and safety indicator of perioperative care, but its documentation in low-and middle-income countries is poor. We developed and tested an electronic, provider report-driven method in an East African country.Methods: We deployed a data collection tool in a Kenyan tertiary referral hospital that collects case-specific perioperative data, with asynchronous automatic transmission to central servers. Cases not captured by the tool (nonobserved) were collected manually for the last two quarters of the data collection period. We created logistic regression models to analyze the impact of procedure type on mortality.Results: Between January 2014 and September 2015, 8,419 cases out of 11,875 were captured. Quarterly data capture rates ranged from 423 (26%) to 1,663 (93%) in the last quarter. There were 93 deaths (1.53%) reported at 7 days. Compared with four deaths (0.53%) in cesarean delivery, general surgery (n = 42 [3.65%]; odds ratio = 15.80 [95% CI, 5.20 to 48.10]; P < 0.001), neurosurgery (n = 19 [2.41%]; odds ratio = 14.08 [95% CI, 4.12 to 48.10]; P < 0.001), and emergency surgery (n = 25 [3.63%]; odds ratio = 4.40 [95% CI, 2.46 to 7.86]; P < 0.001) carried higher risks of mortality. The nonobserved group did not differ from electronically captured cases in 7-day mortality (n = 1 [0.23%] vs. n = 16 [0.58%]; odds ratio = 3.95 [95% CI, 0.41 to 38.20]; P = 0.24).Conclusions: We created a simple solution for high-volume, prospective electronic collection of perioperative data in a lower-to middle-income setting. We successfully used the tool to collect a large repository of cases from a single center in Kenya and observed mortality rate differences between surgery types.