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Bauer ME, Fuller M, Kovacheva V, Elkhateb R, Azar K, Caldwell M, Chiem V, Foster M, Gibbs R, Hughes BL, Johnson R, Kottukapally N, Rosenstein MG, Cortes MS, Shields LE, Sudat S, Sutton CD, Toledo P, Traylor A, Wharton K, Main E. Performance Characteristics of Sepsis Screening Tools During Antepartum and Postpartum Admissions. Obstet Gynecol 2024; 143:336-345. [PMID: 38086052 PMCID: PMC10922108 DOI: 10.1097/aog.0000000000005480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/19/2023] [Indexed: 02/17/2024]
Abstract
OBJECTIVE To evaluate the performance characteristics of existing screening tools for the prediction of sepsis during antepartum and postpartum readmissions. METHODS This was a case-control study using electronic health record data obtained between 2016 and 2021 from 67 hospitals for antepartum sepsis admissions and 71 hospitals for postpartum readmissions up to 42 days. Patients in the sepsis case group were matched in a 1:4 ratio to a comparison cohort of patients without sepsis admitted antepartum or postpartum. The following screening criteria were evaluated: the CMQCC (California Maternal Quality Care Collaborative) initial sepsis screen, the non-pregnancy-adjusted SIRS (Systemic Inflammatory Response Syndrome), the MEWC (Maternal Early Warning Criteria), UKOSS (United Kingdom Obstetric Surveillance System) obstetric SIRS, and the MEWT (Maternal Early Warning Trigger Tool). Time periods were divided into early pregnancy (less than 20 weeks of gestation), more than 20 weeks of gestation, early postpartum (less than 3 days postpartum), and late postpartum through 42 days. False-positive screening rates, C-statistics, sensitivity, and specificity were reported for each overall screening tool and each individual criterion. RESULTS We identified 525 patients with sepsis during an antepartum hospitalization and 728 patients with sepsis during a postpartum readmission. For early pregnancy and more than 3 days postpartum, non-pregnancy-adjusted SIRS had the highest C-statistics (0.78 and 0.83, respectively). For more than 20 weeks of gestation and less than 3 days postpartum, the pregnancy-adjusted sepsis screening tools (CMQCC and UKOSS) had the highest C-statistics (0.87-0.94). The MEWC maintained the highest sensitivity rates during all time periods (81.9-94.4%) but also had the highest false-positive rates (30.4-63.9%). The pregnancy-adjusted sepsis screening tools (CMQCC, UKOSS) had the lowest false-positive rates in all time periods (3.9-10.1%). All tools had the lowest C-statistics in the periods of less than 20 weeks of gestation and more than 3 days postpartum. CONCLUSION For admissions early in pregnancy and more than 3 days postpartum, non-pregnancy-adjusted sepsis screening tools performed better than pregnancy-adjusted tools. From 20 weeks of gestation through up to 3 days postpartum, using a pregnancy-adjusted sepsis screening tool increased sensitivity and minimized false-positive rates. The overall false-positive rate remained high.
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Affiliation(s)
- Melissa E Bauer
- Department of Anesthesiology and the Department of Obstetrics and Gynecology, Duke University, Durham, North Carolina; the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, Massachusetts; the Department of Anesthesiology, University of Arkansas for Medical Sciences, Little Rock, Arkansas; the Sutter Health Institute for Advancing Health Equity and the Center for Health Systems Research, Sutter Health, Sacramento, Common Spirit Health, the Department of Systems Clinical Informatics, Common Spirit Health, the Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, and the Department of Obstetrics and Gynecology, Stanford University School of Medicine, Palo Alto, California; the Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, Texas; and Wayne State University School of Medicine, Wayne, and the Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, Michigan
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Wilson JM, He J, Flowers KM, Kovacheva V, Soens M, Schreiber KL. Pain Severity and Pain Interference in Late Pregnancy: An Analysis of Biopsychosocial Factors Among Women Scheduled for Cesarean Delivery. Pain Med 2023; 24:652-660. [PMID: 36331346 PMCID: PMC10233490 DOI: 10.1093/pm/pnac171] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/31/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2023]
Abstract
OBJECTIVE Pain is a variably experienced symptom during pregnancy, and women scheduled for cesarean delivery, an increasingly common procedure, are a relatively understudied group who might be at higher pain risk. Although biopsychosocial factors are known to modulate many types of chronic pain, their contribution to late pregnancy pain has not been comprehensively studied. We aimed to identify biopsychosocial factors associated with greater pain severity and interference during the last week of pregnancy. METHODS In this prospective, observational study, 662 pregnant women scheduled for cesarean delivery provided demographic and clinical information and completed validated psychological and pain assessments. Multivariable hierarchical linear regressions assessed independent associations of demographic, clinical, and psychological characteristics with pain severity and pain interference during the last week of pregnancy. RESULTS Women in the study had a mean age of 34 years, and 73% identified as White, 11% as African American, 10% as Hispanic/Latina, and 6% as Asian. Most women (66%) were scheduled for repeat cesarean delivery. Significant independent predictors of worse pain outcomes included identifying as African American or Hispanic/Latina and having greater depression, sleep disturbance, and pain catastrophizing. Exploratory analyses showed that women scheduled for primary (versus repeat) cesarean delivery reported higher levels of anxiety and pain catastrophizing. CONCLUSIONS Independent of demographic or clinical factors, psychological factors, including depression, sleep disturbance, and pain catastrophizing, conferred a greater risk of late pregnancy pain. These findings suggest that women at higher risk of pain during late pregnancy could benefit from earlier nonpharmacological interventions that concurrently focus on psychological and pain symptoms.
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Affiliation(s)
- Jenna M Wilson
- Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jingui He
- Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - K Mikayla Flowers
- Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Vesela Kovacheva
- Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Mieke Soens
- Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Kristin L Schreiber
- Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Fortunato A, King L, Mallo D, Kovacheva V, Yuan Y, Boddy A, Graham T, Aktipis A, Mardis ER, Hall A, Marks JR, Hwang S, Maley CC. Abstract P1-05-30: Genomic and microenvironmental intra-tumor heterogeneity in DCIS. Cancer Res 2017. [DOI: 10.1158/1538-7445.sabcs16-p1-05-30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Intra-tumor heterogeneity drives neoplastic progression by supplying the fuel for natural selection among neoplastic cells. It also complicates screening and treatment of neoplasms. We hypothesize that the degree of intra-tumor heterogeneity in DCIS should predict which tumors are likely to become invasive and metastatic. We initiated a pilot project to test this hypothesis by comparing 9 cases of pure DCIS to 9 cases of DCIS with adjacent invasive disease. For each case, we sequenced the exome from two spatially distinct regions of DCIS as well as normal tissue taken from a lymph node with no tumor involvement. This required the development of new methods to extract high quality sequencing data from small amounts of DNA extracted from FFPE samples. We calculated the genetic divergence between the two tumor regions, defined as percent of the sequenced regions of the genome showing differences between the two samples that had sufficient sequencing coverage and quality scores for confident scoring. We also employed automated imaging analysis to score microenvironmental differences between the two tumor regions. These microenvironmental measures are based on ecological methods for measuring organismal interactions and habitats. We will present initial data on differences in phenotypic and genotypic intra-tumor heterogeneity comparing pure DCIS to DCIS associated with invasive breast cancer. Our methods can be readily translated to large tissue banks of FFPE samples from DCIS.
Citation Format: Fortunato A, King L, Mallo D, Kovacheva V, Yuan Y, Boddy A, Graham T, Aktipis A, Mardis ER, Hall A, Marks JR, Hwang S, Maley CC. Genomic and microenvironmental intra-tumor heterogeneity in DCIS [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-05-30.
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Affiliation(s)
- A Fortunato
- Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ; Duke University, Durham, NC; The Institute of Cancer Research, London, United Kingdom; Arizona State University, Tempe, AZ; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom; McDonnell Genome Institute, Washington University, St. Louis, MO
| | - L King
- Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ; Duke University, Durham, NC; The Institute of Cancer Research, London, United Kingdom; Arizona State University, Tempe, AZ; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom; McDonnell Genome Institute, Washington University, St. Louis, MO
| | - D Mallo
- Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ; Duke University, Durham, NC; The Institute of Cancer Research, London, United Kingdom; Arizona State University, Tempe, AZ; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom; McDonnell Genome Institute, Washington University, St. Louis, MO
| | - V Kovacheva
- Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ; Duke University, Durham, NC; The Institute of Cancer Research, London, United Kingdom; Arizona State University, Tempe, AZ; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom; McDonnell Genome Institute, Washington University, St. Louis, MO
| | - Y Yuan
- Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ; Duke University, Durham, NC; The Institute of Cancer Research, London, United Kingdom; Arizona State University, Tempe, AZ; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom; McDonnell Genome Institute, Washington University, St. Louis, MO
| | - A Boddy
- Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ; Duke University, Durham, NC; The Institute of Cancer Research, London, United Kingdom; Arizona State University, Tempe, AZ; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom; McDonnell Genome Institute, Washington University, St. Louis, MO
| | - T Graham
- Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ; Duke University, Durham, NC; The Institute of Cancer Research, London, United Kingdom; Arizona State University, Tempe, AZ; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom; McDonnell Genome Institute, Washington University, St. Louis, MO
| | - A Aktipis
- Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ; Duke University, Durham, NC; The Institute of Cancer Research, London, United Kingdom; Arizona State University, Tempe, AZ; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom; McDonnell Genome Institute, Washington University, St. Louis, MO
| | - ER Mardis
- Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ; Duke University, Durham, NC; The Institute of Cancer Research, London, United Kingdom; Arizona State University, Tempe, AZ; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom; McDonnell Genome Institute, Washington University, St. Louis, MO
| | - A Hall
- Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ; Duke University, Durham, NC; The Institute of Cancer Research, London, United Kingdom; Arizona State University, Tempe, AZ; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom; McDonnell Genome Institute, Washington University, St. Louis, MO
| | - JR Marks
- Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ; Duke University, Durham, NC; The Institute of Cancer Research, London, United Kingdom; Arizona State University, Tempe, AZ; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom; McDonnell Genome Institute, Washington University, St. Louis, MO
| | - S Hwang
- Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ; Duke University, Durham, NC; The Institute of Cancer Research, London, United Kingdom; Arizona State University, Tempe, AZ; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom; McDonnell Genome Institute, Washington University, St. Louis, MO
| | - CC Maley
- Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ; Duke University, Durham, NC; The Institute of Cancer Research, London, United Kingdom; Arizona State University, Tempe, AZ; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom; McDonnell Genome Institute, Washington University, St. Louis, MO
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