Monitoring Anesthesia Care Delivery and Perioperative Mortality in Kenya Utilizing a Provider-driven Novel Data Collection Tool

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 deploye...

Full description

Saved in:

Bibliographic Details
Published in:	Anesthesiology (Philadelphia) Vol. 127; no. 2; pp. 250 - 271
Main Authors:	Sileshi, Bantayehu, Newton, Mark W, Kiptanui, Joash, Shotwell, Matthew S, Wanderer, Jonathan P, Mungai, Mary, Scherdin, Jon, Harris, Paul A, Vermund, Sten H, Sandberg, Warren S, McEvoy, Matthew D
Format:	Journal Article
Language:	English
Published:	United States Copyright by , the American Society of Anesthesiologists, Inc. Wolters Kluwer Health, Inc 01-08-2017
Subjects:	Adolescent Adult Aged Anesthesia - methods Anesthesia - mortality Child Child, Preschool Data Collection - methods Female Hospital Mortality Humans Infant Infant, Newborn Kenya Male Middle Aged Monitoring, Physiologic - methods Perioperative Care - methods Perioperative Period Prospective Studies Quality Assurance, Health Care - methods Risk Factors Young Adult
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0003-3022 1528-1175
DOI:	10.1097/ALN.0000000000001713