Feasibility of Routine Data Collection on Intensive Care Unit Performance and Activity in Resource Limited Settings
Background: Routine collection and analysis of data allows a critical care department to highlight the outcomes of the interventions done and to identify the grounds for improvement. Data on characteristic and outcomes of patients admitted in intensive care units (ICUs) are lacking.
Methods: A software (ICU e-monitoring®) was designed to enter for each patient demographic data, SAPS3 on admission, Nine Equivalent Manpower Use Score, presence of medical devices and episodes of hospital acquired infections. We report data collected during 2014 with comparison to data collected with the same methodology in 2008 .
Objective: To determine the standardized mortality ratio, the mean length of ICU stay, mean length of mechanical ventilation and ICU acquired infection incidence rate.
Study design: Descriptive
Place of study: Medical ICU, Pakistan Institute of Medical Sciences Islamabad
Results: A total of 196 admissions were recorded during the year 2014 vs 354 in 2008. 47.2% were males and 52.8% were females. Mean age was 32.1 years ± 15.3 SD (37.7 ± 18.9 SD in 2008). A total of 65 (33%) deaths were recorded during the year and standardized mortality ratio was found to be 0.71 vs 1.09 in 2008. Mean Length of stay was 15.9 Days ± 12.9 SD (9.3 days ± 8.9 in 2008) and mean duration of mechanical ventilation was found to be 12.04 Days (8.7 in 2008). Overall ventilator associated pneumonia (VAP) rate was 42.3 cases per 1000 ventilator days. Rate of Catheter Related Blood Stream Infections (CRBSI) was found to be 17.2 cases per 1000 CVC days.
Conclusion: Major changes in our patient population characteristics were seen between 2008 and 2013: number of patients and standardized mortality was decreased while incidence of VAP and CRBSI was increased. It is possible to collect meaningful data on ICU performance and activity in resource limited settings.
2. Flagg AJ. The role of patient-centered care in nursing. Nurs Clin North Am. 2015;50(1):75-86.
3. Duffy JR, Kooken WC, Wolverton CL, Weaver MT. Evaluating patient-centered care: feasibility of electronic data collection in hospitalized older adults. J Nurs Care Qual. 2012;27(4):307-15.
4. Saarinen K, Aho M. Does the implementation of a clinical information system decrease the time intensive care nurses spend on documentation of care? Acta Anaesthesiol Scand. 2005;49(1):62-5.
5. Mador RL, Shaw NT. The impact of a Critical Care Information System (CCIS) on time spent charting and in direct patient care by staff in the ICU: a review of the literature. Int J Med Inform. 2009;78(7):435-45.
6. Bosman R.J. Impact of computerized information systems on workload in operating room and intensive care unit. Best Pract Res Clin Anaesthesiol. 2009;23(1):15-26
7. Meyfroidt G. How to implement information technology in the operating room and the intensive care unit. Best Pract Res Clin Anaesthesiol. 2009;23(1):1-14.
8. John C, Rodney WH. Computer-Related Medication Errors in Neonatal Intensive Care Units. Clinics in Perinatology. 2008;35(1):119-139
9. Taylor JA, Loan LA, Kamara J, Blackburn S, Whitney D. Medication administration variances before and after implementation of computerized physician order entry in a neonatal intensive care unit. Pediatrics. 2008;121(1):123-8
10. Ioos V. Intensive Care Medicine in Resource Limited Health Systems: Experience of a Pakistani-French Cooperation Program in Intensive Care. Réanimation. 2014;23(5):466–75
11. Lee JW, LaRoche S, Choi H, Rodriguez Ruiz AA, Fertig E, Politsky J, et al. Development and validation of a critical care EEG monitoring database for standardized clinical reporting and multicenter collaborative research. J Clin Neurophysiol. 2016; 33(2): 130-40.
12. Brunsveld-Reinders AH, Arbous MS, Kuiper SG, de Jonge E. A comprehensive method to develop a checklist to increase safety of intra-hospital transport of critically ill patients. Crit Care. 2015; 19:214.
13. Serpa Neto A, Assunção MS, Pardini A, Silva E. Feasibility of transitioning from APACHE II to SAPS III as prognostic model in a Brazilian general intensive care unit. A retrospective study. Sao Paulo Med J. 2015;133(3):199-205.
14. Smischney NJ, Velagapudi VM, Onigkeit JA, Pickering BW, Herasevich V, Kashyap R. Derivation and validation of a search algorithm to retrospectively identify mechanical ventilation initiation in the intensive care unit. BMC Med Inform Decis Mak. 2014; 14:55
15. Singh B, Singh A, Ahmed A, Wilson GA, Pickering BW, Herasevich V, et al. Derivation and validation of automated electronic search strategies to extract Charlson comorbidities from electronic medical records. Mayo Clin Proc. 2012;87(9):817-24.
16. Blackwood B, Burns KE, Cardwell CR, O'Halloran P. Protocolized versus non-protocolized weaning for reducing the duration of mechanical ventilation in critically ill adult patients. Cochrane Database Syst Rev. 2014;11:CD006904.
17. Rose L, Schultz MJ, Cardwell CR, Jouvet P, McAuley DF, Blackwood B. Automated versus non-automated weaning for reducing the duration of mechanical ventilation for critically ill adults and children. Cochrane Database Syst Rev. 2014;6:CD009235.
18. Moran JL, Bristow P, Solomon PJ, George C, Hart GK; Australian and New Zealand Intensive Care Society Database Management Committee (ADMC). Mortality and length-of-stay outcomes, 1993-2003, in the binational Australian and New Zealand intensive care adult patient database. Crit Care Med. 2008; 36(1):46-61.
19. Bird GT, Farquhar-Smith P, Wigmore T, Potter M, Gruber PC. Outcomes and prognostic factors in patients with haematological malignancy admitted to a specialist cancer intensive care unit: a 5 yr study. Br J Anaesth. 2012;108(3):452-9.
20. Rosenthal VD, Maki DG, Salomao R, Moreno CA, Mehta Y, Higuera F, et al. Device-Associated Nosocomial Infections in 55 Intensive Care Units of 8 Developing Countries. Ann Intern Med.2006;145(8):582-91.