DOI: 10.1055/s-0039-1683363
Finding Understudied Disorders Potentially Associated with Maternal Morbidity and Mortality
Publication History
15 January 2019
26 January 2019
Publication Date:
04 March 2019 (online)
Abstract
Objective Clinical research literature focuses primarily on the most common causes of maternal morbidity and mortality (MMM). We explore sections of the discharge summaries of pregnant or postpartum women admitted to an intensive care unit (ICU) to identify associated disorders and mine the literature to identify knowledge gaps in clinical research.
Methods Data for the study were discharge summaries in the MIMIC (Medical Information Mart for Intensive Care) database. We extracted a control cohort to study if there is a difference in comorbidities between pregnant and not pregnant patients with similar reasons for admission. We identified comorbidities of the Unified Medical Language System (UMLS) semantic types disease or syndrome, Mental or behavioral dysfunction, and injury, or poisoning. We used Entrez programming utilities (E-utilities) to query PubMed®.
Results We identified 246 pregnant and postpartum patients. A control group of 587 not pregnancy related admissions matched on age and admit diagnosis. We found overlap of 24.3% discharge diagnoses between the two groups, and 7.5% of the codes exclusively in the pregnancy group. We identified 33 disease mentions not included in the most common reported causes of MMM.
Conclusion Our results demonstrate that clinical text provides additional comorbidities associated with maternal complications that need further clinical research.
References
- 1 Pregnancy mortality surveillance system. Centers for disease control and prevention. Available from: https://www.cdc.gov/reproductivehealth/maternalinfanthealth/pmss.html . Accessed February 11, 2019
- 2 Main EK, Menard MK. Maternal mortality: time for national action. Obstet Gynecol 2013; 122 (04) 735-736
- 3 Preventing maternal deaths act of 2018. One hundred fifteenth congress of the United States of America. 2019. Available from: https://www.congress.gov/bill/115th-congress/house-bill/1318/text . Accessed February 11, 2019
- 4 Mhyre JM, Bateman BT, Leffert LR. Influence of patient comorbidities on the risk of near-miss maternal morbidity or mortality. Anesthesiology 2011; 115 (05) 963-972
- 5 Evaluating the quality of care for severe pregnancy complications. World Health Organization; 2011. Available from: http://apps.who.int/iris/bitstream/10665/44692/1/9789241502221_eng.pdf . Accessed February 11, 2019
- 6 Say L, Souza JP, Pattinson RC. ; WHO working group on Maternal Mortality and Morbidity classifications. Maternal near miss--towards a standard tool for monitoring quality of maternal health care. Best Pract Res Clin Obstet Gynaecol 2009; 23 (03) 287-296
- 7 Clark SL, Meyers JA, Frye DR, McManus K, Perlin JB. A systematic approach to the identification and classification of near-miss events on labor and delivery in a large, national health care system. Am J Obstet Gynecol 2012; 207 (06) 441-445
- 8 Bale J, Stoll B, Lucas A. Improving Birth Outcomes: Meeting the Challenge in the Developing World. Committee on Improving Birth Outcomes Institute of Medicine (US). 1st ed. Washington DC, WA: National Academic Press; 2003
- 9 MacDorman MF, Declercq E, Cabral H, Morton C. Recent increases in the U.S. maternal mortality rate: disentangling trends from measurement issues. Obstet Gynecol 2016; 128 (03) 447-455
- 10 Oud L. Epidemiology of pregnancy-associated ICU utilization in Texas: 2001 - 2010. J Clin Med Res 2017; 9 (02) 143-153
- 11 Wanderer JP, Leffert LR, Mhyre JM, Kuklina EV, Callaghan WM, Bateman BT. Epidemiology of obstetric-related ICU admissions in Maryland: 1999-2008*. Crit Care Med 2013; 41 (08) 1844-1852
- 12 Taft LM, Evans RS, Shyu CR. , et al. Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery. J Biomed Inform 2009; 42 (02) 356-364
- 13 Gil-González D, Carrasco-Portiño M, Ruiz MT. Knowledge gaps in scientific literature on maternal mortality: a systematic review. Bull World Health Organ 2006; 84 (11) 903-909
- 14 Rodriguez L, Demner Fushman D. Extracting diseases co-occurring with past medical history of Lyme disease. AMIA Annu Symp Proc 2018. Available at: https://informaticssummit2018.zerista.com/event/member/470616 . Accessed February 21, 2019
- 15 Saeed M, Villarroel M, Reisner AT. , et al. Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database. Crit Care Med 2011; 39 (05) 952-960
- 16 Ide NC, Loane RF, Demner-Fushman D. Essie: a concept-based search engine for structured biomedical text. J Am Med Inform Assoc 2007; 14 (03) 253-263
- 17 HCUP CCS. Clinical classification software ICD-9-CM. Available from: www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp . Accessed February 11, 2019
- 18 Edinger T, Demner-Fushman D, Cohen AM, Bedrick S, Hersh W. Evaluation of clinical text segmentation to facilitate cohort retrieval. AMIA Annu Symp Proc 2018; 2017: 660-669
- 19 Thanaki J. Python Natural Language Processing. Mumbai, India: Packt; 2017
- 20 BRAT: a web-based tool for NLP-assisted text annotation. Available from: http://aclweb.org/anthology/E/E12/E12-2021.pdf . Accessed February 11, 2019
- 21 Unified medical language system (UMLS). Available from: http://www.nlm.nih.gov/research/umls/ . Accessed February 11, 2019
- 22 Entrez programming utilities help. Available from: https://www.ncbi.nlm.nih.gov/books/NBK25501/ . Accessed February 11, 2019
- 23 Bethesda P. (MD): National Library of Medicine (US). 1946 Available from: http://pubmed.gov/ . Accessed February 11, 2019