Perioperative Medicine  |   August 2017
Monitoring Anesthesia Care Delivery and Perioperative Mortality in Kenya Utilizing a Provider-driven Novel Data Collection Tool
Author Notes
  • From the Departments of Anesthesiology (B.S., M.W.N., J.P.W., W.S.S., M.D.M.), Biostatistics (M.S.S.), Biomedical Informatics (J.S., P.A.H.), and Pediatrics (S.H.V.), Vanderbilt University Medical Center, Nashville, Tennessee; Department of Anesthesiology, Kijabe Africa Inland Church Hospital, Kijabe, Kenya (M.W.N., J.K., M.M.); and Vanderbilt Institute for Clinical and Translational Research (J.S., P.A.H.) and Vanderbilt Institute for Global Health (S.H.V.), Vanderbilt University School of Medicine, Nashville, Tennessee.
  • This article is featured in “This Month in Anesthesiology,” page 1A.
    This article is featured in “This Month in Anesthesiology,” page 1A.×
  • Corresponding article on page 215.
    Corresponding article on page 215.×
  • Submitted for publication August 10, 2016. Accepted for publication April 7, 2017.
    Submitted for publication August 10, 2016. Accepted for publication April 7, 2017.×
  • Address correspondence to Dr. Sileshi: Vanderbilt University Medical Center, Cardiothoracic Anesthesiology, 1215 21st Avenue South, Suite 5160 MCE North Tower, Nashville, Tennessee 37232. bantayehu.sileshi@vanderbilt.edu. Information on purchasing reprints may be found at www.anesthesiology.org or on the masthead page at the beginning of this issue. Anesthesiology’s articles are made freely accessible to all readers, for personal use only, 6 months from the cover date of the issue.
Article Information
Perioperative Medicine / Clinical Science / Gastrointestinal and Hepatic Systems / Neurosurgical Anesthesia / Obstetric Anesthesia / Pediatric Anesthesia / Trauma / Burn Care / Quality Improvement
Perioperative Medicine   |   August 2017
Monitoring Anesthesia Care Delivery and Perioperative Mortality in Kenya Utilizing a Provider-driven Novel Data Collection Tool
Anesthesiology 8 2017, Vol.127, 250-271. doi:10.1097/ALN.0000000000001713
Anesthesiology 8 2017, Vol.127, 250-271. doi:10.1097/ALN.0000000000001713
Abstract

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.