Secular Trends in An Indian Intensive Care Unit-database Derived Epidemiology: The Stride Study
Manu Varma MK
Keywords :
Customized Health in Intensive Care Trainable Research and Analysis tool (CHITRA), International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD 10), Admission Diagnosis, Comorbidity, Intensive Care Unit (ICU)
Citation Information :
MK MV. Secular Trends in An Indian Intensive Care Unit-database Derived Epidemiology: The Stride Study. Indian J Crit Care Med 2019; 23 (6):251-257.
Context: The Indian Society of Critical Care Medicine (ISCCM), had taken an initiative to enable all Indian ICUs (Intensive Care Unit) to capture and store relevant data in a systematic manner in an electronic database: “CHITRA” (Customized Health in Intensive Care Trainable Research and Analysis tool).
Aims: This study was aimed at capturing, and summarising longitudinal epidemiological data from a single tertiary care hospital ICU (Intensive Care Unit), based on a pre-existing database and the CHITRA (Customized Health in Intensive Care Trainable Research and Analysis tool) system. Settings and design: Prospective Observational
Methods and material: Data was extracted from two databases, a pre-existing database, arbitrarily named pre-CHITRA (January 2006 to April 2014), and the CHITRATM database (October 2015 to January 2018). Diagnoses of the patients admitted were tabulated using the ICD10 (International Statistical Classification of Diseases and Related Health Problems 10th Revision) coding format. The outcomes were summarised and cross tabulated.
Statistical analysis used: Cross tabulations were used to display summarized data, analysis of outcomes were done using t test and regression analyses, and correspondence analysis was used to explore associations of descriptors.
Results: A total of 18940 patients were admitted, with a male preponderance, and the median age was fifty-two years. Most of admissions were from emergency (62%). The age (0.3, p = 0.000, CI (0.2 - 0.38)) and mean APACHE II score of patients had increased over the years (0.18, p = 0.000 CI (0.12-0.25). The ICU mortality had decreased significantly over the years (–0.04, p = 0.000, CI (–0.05 to –0.03)). The most common admission diagnosis in the pre-CHITRA database was general symptoms and signs (ICD10 R50-R69), and in the CHITRA database was Type 1 Respiratory failure (ICD 10 J96.90).
Conclusion: This study has shown the utility of the CHITRA system in capturing epidemiological data from a single centre.
Wunsch H, Angus DC, Harrison DA, Collange O, Fowler R, Hoste EA, et al. Variation in critical care services across North America and Western Europe. Crit Care Med. 2008;36(10):2787–2793. doi: 10.1097/CCM.0b013e318186aec8
Hartl WH, Wolf H, Schneider CP, Küchenhoff H, Jauch K-W. Secular trends in mortality associated with new therapeutic strategies in surgical critical illness. Am J Surg. 2007;194(4):535–541. doi: 10.1016/j. amjsurg.2006.12.043
Olaechea PM, Álvarez-Lerma F, Palomar M, Gimeno R, Gracia MP, Mas N, et al. Characteristics and outcomes of patients admitted to Spanish ICU: A prospective observational study from the ENVINHELICS registry (2006-2011). Med Intensiva. 2016;40(4):216–229. doi: 10.1016/j.medin.2015.07.003
Martin G. Epidemiology studies in critical care. Crit Care. 2006;10(2):136–136.
Dragsted L, Qvist J. Epidemiology of Intensive Care. Int J Technol Assess Health Care.. 1992;8(3):395–407.
Zimmerman JE, Kramer AA, Knaus WA. Changes in hospital mortality for United States intensive care unit admissions from 1988 to 2012. Crit Care. 2013;17(2):R81. doi: 10.1186/cc12695
Divatia JV, Amin PR, Ramakrishnan N, Kapadia FN, Todi S, Sahu S, et al. Intensive care in India: the Indian intensive care case mix and practice patterns study. Indian J Crit Care Med.2016;20(4):216–225. doi: 10.4103/0972-5229.180042
Karnad DR, Lapsia V, Krishnan A, Salvi VS. Prognostic factors in obstetric patients admitted to an Indian intensive care unit. Crit Care Med. 2004;32(6):1294–1299.
Harrison DA, Brady AR, Rowan K. Case mix, outcome and length of stay for admissions to adult, general critical care units in England, Wales and Northern Ireland: The Intensive Care National Audit & Research Centre Case Mix Programme Database. Critical Care. 2004;8(2):R99– R111. doi: 10.1186/cc2834
Lapinsky SE, Holt D, Hallett D, Abdolell M, Adhikari NK. Survey of information technology in intensive care units in Ontario, Canada. BMC Med Inform Decis Mak. 2008;8(1):5. doi: 10.1186/1472-6947-8-5
Sharma M, Aggarwal H. EHR Adoption in India: Potential and the Challenges. Indian J Sci Technol. 2016;9(34):1–7. doi: 10.17485/ijst/2016/v9i34/100211
CHITRA [Online]. Indian Society of Critical Care Medicine. Available from: http://www.isccm.org/chitra.aspx [Last Accessed May 2019]
WHO.List of Official ICD-10 Updates [Online]. WHO.. Available from: http://www.who.int/classifications/icd/icd10updates/en/ [Last Accessed May 2019]
UMLS Terminology Services -- Home [Online]. Available from: https://uts.nlm.nih.gov/home.html [Last Accessed May 2019].
Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Critical care medicine. 1985;13(10):818–829.
StataCorp L. College Station, TX: StataCorp LP; 2014. Stata survey data reference manual: release. 14.
Team RC. R: A language and environment for statistical computing. 2013; 201
Nenadic O, Greenacre M. Correspondence analysis in R, with two-and three-dimensional graphics: The ca package. J Stat Softw. 2007;20(3):1–13.
Vincent J-L, Marshall JC, Ñamendys-Silva SA, François B, Martin- Loeches I, Lipman J, et al. Assessment of the worldwide burden of critical illness: the intensive care over nations (ICON) audit. Lancet Respir Med. 2014;2(5):380–386. doi: 10.1016/S2213-2600(14)70061-X
Park J, Jeon K, Chung CR, Yang JH, Cho YH, Cho J, et al. A nationwide analysis of intensive care unit admissions, 2009–2014–The Korean ICU National Data (KIND) study. J Crit Care. 2018;44:24–30. doi: 10.1016/j. jcrc.2017.09.017
Valentin A, Jordan B, Lang T, Hiesmayr M, Metnitz PG. Genderrelated differences in intensive care: a multiple-center cohort study of therapeutic interventions and outcome in critically ill patients. Crit Care Med.. 2003;31(7):1901–1907. doi: 10.1097/01. CCM.0000069347.78151.50
Mahmood K, Eldeirawi K, Wahidi MM. Association of gender with outcomes in critically ill patients. Crit Care. 2012;16(3):R92. doi: 10.1186/cc11355
Strand K, Walther SM, Reinikainen M, Ala-Kokko T, Nolin T, Martner J, et al. Variations in the length of stay of intensive care unit nonsurvivors in three Scandinavian countries. Crit care. 2010;14(5):R175. doi: 10.1186/cc9279
Sampath S, Fay M, Pais P. Use of the logistic organ dysfunction system to study mortality in an Indian intensive care unit. Natl Med J India. 1999;12(6):258–261.
Lilly CM, Swami S, Liu X, Riker RR, Badawi O. Five-Year Trends of Critical Care Practice and Outcomes. Chest. 2017;152(4):723–735.
Vincent J-L, Rello J, Marshall J, Silva E, Anzueto A, Martin CD, et al. International study of the prevalence and outcomes of infection in intensive care units. Jama. 2009;302(21):2323–2329.
Greenacre M. Correspondence analysis of the Spanish National Health Survey. Gac Sanit. 2002;16(2):160–170.
Friedman HP. Strategies for Multivariate Data Analysis: Case Studies. In: Acquisition, Analysis and Use of Clinical Transplant Data. Springer;