{"id":20034,"date":"2023-10-17T01:43:22","date_gmt":"2023-10-17T01:43:22","guid":{"rendered":"https:\/\/academicwritersbay.com\/writings\/read-the-articleoncologic-emergencies-in-a-cancer-center-emergency-department\/"},"modified":"2023-10-17T01:43:22","modified_gmt":"2023-10-17T01:43:22","slug":"read-the-articleoncologic-emergencies-in-a-cancer-center-emergency-department","status":"publish","type":"post","link":"https:\/\/academicwritersbay.com\/writings\/read-the-articleoncologic-emergencies-in-a-cancer-center-emergency-department\/","title":{"rendered":"Read the article,?Oncologic emergencies in a cancer center emergency department"},"content":{"rendered":"
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\u00a0<\/p>\n

Instructions<\/strong><\/p>\n

Read the article,\u00a0\u201cOncologic emergencies in a cancer center emergency department and in general emergency departments countywide and nationwide\u201d.\u00a0 You may access the article in the resource folder.\u00a0<\/p>\n

Thoroughly review the complete research article exploring the management of oncologic emergencies in critically ill patients visiting the Emergency Department (ED). Focus on patient characteristics, diagnoses, and factors influencing hospitalization.<\/p>\n

Your case study should demonstrate a thorough grasp of the entire article, using specific details to support your points. This assignment aims to assess your ability to distill complex medical information into a focused case study, highlighting essential strategies for managing oncologic emergencies in critically ill patients.\u00a0<\/p>\n

Please make sure to provide citations and references (in APA, 7th ed. format) for your work.\u00a0\u00a0<\/p>\n

Case Study Focus:<\/p>\n

Based on your comprehensive reading, create a Case study highlighting key strategies for effectively managing oncologic emergencies in critically ill patients.<\/p>\n

Key Points<\/strong><\/p>\n

Resource Optimization: Explain how increased resource utilization and hospitalization rates among cancer patients impact healthcare delivery. Discuss strategies to optimize resources while maintaining high quality care.<\/p>\n

Timely Diagnosis: Emphasize the importance of timely diagnosis and intervention. Provide examples from the article illustrating the benefits of early recognition and swift treatment.<\/p>\n

Tailored Interventions: Explore the concept of tailoring treatments to individual oncologic conditions. Share insights on customizing interventions based on diagnoses outlined in the article.<\/p>\n

Multidisciplinary Collaboration: Discuss the value of multidisciplinary collaboration in managing critically ill patients. Describe how different specialties can work together for comprehensive patient care.<\/p>\n

Risk Stratification: Explain the role of risk assessment in identifying high-risk patients and the significance of early interventions to prevent complications.<\/p>\n

Education and Training: Address the impact of education and training on enhancing oncologic emergencies management. Highlight the benefits of well informed healthcare professionals.<\/p>\n

Conclusion: Summarize key insights from the article and emphasize the patient centered approach to managing oncologic emergencies in critically ill patients.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n

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    Oncologic_emergenciespdf..pdf<\/div>\n<\/p><\/div>\n<\/li>\n<\/ul><\/div>\n<\/p><\/div>\n
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    RESEARCH ARTICLE <\/p>\n

    Oncologic emergencies in a cancer center <\/p>\n

    emergency department and in general <\/p>\n

    emergency departments countywide and <\/p>\n

    nationwide <\/p>\n

    Zhi Yang1\u00a4a, Runxiang Yang1\u00a4b, Min Ji Kwak1\u00a4c, Aiham Qdaisat1, Junzhong Lin1\u00a4d, Charles <\/p>\n

    E. Begley2, Cielito C. Reyes-Gibby1, Sai-Ching Jim Yeung1,3* <\/p>\n

    1 Department of Emergency Medicine, The University of Texas MD Anderson Cancer Center, Houston, <\/p>\n

    Texas, United States of America, 2 Division of Management, Policy, and Community Health, The University <\/p>\n

    of Texas Health Science Center at Houston School of Public Health, Houston, Texas, United States of <\/p>\n

    America, 3 Department of Endocrine Neoplasia and Hormonal Disorders, The University of Texas MD <\/p>\n

    Anderson Cancer Center, Houston, Texas, United States of America <\/p>\n

    \u00a4a Current address: Department of Intensive Care, Guangzhou First People\u2019s Hospital, Guangzhou Medical <\/p>\n

    University, Guangzhou, Guangdong, People\u2019s Republic of China <\/p>\n

    \u00a4b Current address: Second Department of Medical Oncology, Tumor Hospital of Yunnan Province, <\/p>\n

    Kunming, Yunnan, People\u2019s Republic of China <\/p>\n

    \u00a4c Current address: Department of Medicine, The University of Texas Health Science Center, Houston, <\/p>\n

    Texas, United States of America <\/p>\n

    \u00a4d Current address: Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, Guangzhou, <\/p>\n

    Guangdong, People\u2019s Republic of China <\/p>\n

    * [email\u00a0protected] <\/p>\n

    Abstract <\/p>\n

    Background <\/p>\n

    Although cancer patients (CPs) are increasingly likely to visit emergency department (ED), <\/p>\n

    no population-based study has compared the characteristics of CPs and non-cancer <\/p>\n

    patients (NCPs) who visit the ED and examined factors associated with hospitalization via <\/p>\n

    the ED. In this study, we (1) compared characteristics and diagnoses between CPs and <\/p>\n

    NCPs who visited the ED in a cancer center or general hospital; (2) compared characteris- <\/p>\n

    tics and diagnoses between CPs and NCPs who were hospitalized via the ED in a cancer <\/p>\n

    center or general hospital; and (3) investigated important factors associated with such <\/p>\n

    hospitalization. <\/p>\n

    Methods and findings <\/p>\n

    We analyzed patient characteristic and diagnosis [based on International Classification of <\/p>\n

    Diseases-9 (ICD-9) codes] data from the ED of a comprehensive cancer center (MDACC), <\/p>\n

    24 general EDs in Harris County, Texas (HCED), and the National Hospital Ambulatory <\/p>\n

    Medical Care Survey (NHAMCS) from 1\/1\/2007\u201312\/31\/2009. Approximately 3.4 million ED <\/p>\n

    visits were analyzed: 47,245, 3,248,973, and 104,566 visits for MDACC, HCED, and <\/p>\n

    NHAMCS, respectively, of which 44,143 (93.4%), 44,583 (1.4%), and 632 (0.6%) were CP <\/p>\n

    visits. CPs were older than NCPs and stayed longer in EDs. Lung, gastrointestinal <\/p>\n

    PLOS ONE | https:\/\/doi.org\/10.1371\/journal.pone.0191658 February 20, 2018 1 \/ 14 <\/p>\n

    a1111111111 <\/p>\n

    a1111111111 <\/p>\n

    a1111111111 <\/p>\n

    a1111111111 <\/p>\n

    a1111111111 <\/p>\n

    OPENACCESS <\/p>\n

    Citation: Yang Z, Yang R, Kwak MJ, Qdaisat A, Lin <\/p>\n

    J, Begley CE, et al. (2018) Oncologic emergencies <\/p>\n

    in a cancer center emergency department and in <\/p>\n

    general emergency departments countywide and <\/p>\n

    nationwide. PLoS ONE 13(2): e0191658. https:\/\/ <\/p>\n

    doi.org\/10.1371\/journal.pone.0191658 <\/p>\n

    Editor: Luis Costa, Hospital de Santa Maria, <\/p>\n

    PORTUGAL <\/p>\n

    Received: July 10, 2017 <\/p>\n

    Accepted: January 9, 2018 <\/p>\n

    Published: February 20, 2018 <\/p>\n

    Copyright: This is an open access article, free of all <\/p>\n

    copyright, and may be freely reproduced, <\/p>\n

    distributed, transmitted, modified, built upon, or <\/p>\n

    otherwise used by anyone for any lawful purpose. <\/p>\n

    The work is made available under the Creative <\/p>\n

    Commons CC0 public domain dedication. <\/p>\n

    Data Availability Statement: All relevant data are <\/p>\n

    within the paper and its Supporting Information <\/p>\n

    files. <\/p>\n

    Funding: ZY is supported by Guangzhou First <\/p>\n

    People\u2019s Hospital, Guangzhou Medical University. <\/p>\n

    RY is partially supported by the National Natural <\/p>\n

    Science Foundation of China (81360393 and <\/p>\n

    81560432). JL is supported by Sun Yat-sen <\/p>\n

    University Cancer Center. CRG is the Principal <\/p>\n

    Investigator of and is supported by the Program in <\/p>\n

    Oncologic Emergency Medicine of The University<\/p>\n<\/p>\n

    https:\/\/doi.org\/10.1371\/journal.pone.0191658 <\/div>\n
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    https:\/\/creativecommons.org\/publicdomain\/zero\/1.0\/ <\/div>\n<\/p>\n<\/div>\n
    \n

    (excluding colorectal), and genitourinary (excluding prostate) cancers were the three most <\/p>\n

    common diagnoses related to ED visits at general EDs. CPs visiting MDACC were more <\/p>\n

    likely than CPs visiting HCED to be privately insured. CPs were more likely than NCPs to be <\/p>\n

    hospitalized. Pneumonia and influenza, fluid and electrolyte disorders, and fever were <\/p>\n

    important predictive factors for CP hospitalization; coronary artery disease, cerebrovascular <\/p>\n

    disease, and heart failure were important factors for NCP hospitalization. <\/p>\n

    Conclusions <\/p>\n

    CPs consumed more ED resources than NCPs and had a higher hospitalization rate. Given <\/p>\n

    the differences in characteristics and diagnoses between CPs and NCPs, ED physicians <\/p>\n

    must pay special attention to CPs and be familiar with their unique set of oncologic <\/p>\n

    emergencies. <\/p>\n

    Introduction <\/p>\n

    Given the increasing incidence of and declining mortality rate for cancer worldwide, cancer <\/p>\n

    patients (CPs) are increasingly likely to visit an emergency department (ED), either in cancer <\/p>\n

    centers or general hospitals, at least once to obtain urgent care [1\u20133]. In previous studies, the <\/p>\n

    ED-to-hospitalization rate of CPs (>50%) [1, 4] well exceeded that of non-CPs (NCPs) <\/p>\n

    (11.9%) [5]. Moreover, as CPs have unique sequelae related to their disease and treatment, it is <\/p>\n

    crucial for both general and cancer-specialist ED physicians to better understand the needs of <\/p>\n

    CPs in emergent situations. <\/p>\n

    Research on CP ED visits has focused primarily on cancer type and chief complaints [1, 6, <\/p>\n

    7] end-of-life ED visits [3, 8, 9] or specific cancer types [10\u201312]. Most of this research has <\/p>\n

    focused on commonalities among CPs; to our knowledge, none has compared the characteris- <\/p>\n

    tics of CPs and NCPs who visit the ED, either in cancer centers or general hospitals. Moreover, <\/p>\n

    although several studies have shown that hospitalization via the ED is a clinically important <\/p>\n

    marker of poorer prognosis for CPs [13\u201315], no population-based study has examined factors <\/p>\n

    associated with CP hospitalization via the ED. <\/p>\n

    In this study, we (1) compared characteristics and diagnoses between CPs and NCPs who <\/p>\n

    visited the ED in a cancer center or general hospital; (2) compared characteristics and diagno- <\/p>\n

    ses between CPs and NCPs who were hospitalized via the ED in a cancer center or general hos- <\/p>\n

    pital; and (3) investigated important factors associated with such hospitalization. <\/p>\n

    Methods <\/p>\n

    Data collection <\/p>\n

    We collected data on the characteristics of visitors to the ED at The University of Texas MD <\/p>\n

    Anderson Cancer Center in Houston, Texas, visitors to EDs at general hospitals in Harris <\/p>\n

    County, Texas (which includes Houston), and ED visitors assessed in the US National Hospital <\/p>\n

    Ambulatory Medical Care Survey (NHAMCS). Our study was conducted under a clinical <\/p>\n

    research protocol (DR08-0066) approved by the MD Anderson Institutional Review Board <\/p>\n

    and in compliance with Health Insurance Portability and Accountability Act regulations. As <\/p>\n

    this was a retrospective data review, informed consent requirements were waived. <\/p>\n

    MD Anderson is a specialized referral center for cancer care. Its ED handles ~22,000 patient <\/p>\n

    visits per year; >90% of the ED visitors are MD Anderson patients. Study data (hereafter, <\/p>\n

    Factors for admission for oncology emergencies <\/p>\n

    PLOS ONE | https:\/\/doi.org\/10.1371\/journal.pone.0191658 February 20, 2018 2 \/ 14 <\/p>\n

    of Texas MD Anderson Cancer Center. The <\/p>\n

    University of Texas MD Anderson Cancer Center is <\/p>\n

    supported in part by the National Institutes of <\/p>\n

    Health through Cancer Center Support Grant P30 <\/p>\n

    CA016672. The funders had no role in study <\/p>\n

    design, data collection and analysis, decision to <\/p>\n

    publish, or preparation of the manuscript. <\/p>\n

    Competing interests: Dr. Yeung is the principal <\/p>\n

    investigator of an investigator-initiated clinical trial <\/p>\n

    supported by DepoMed and a retrospective clinical <\/p>\n

    study supported by Bristol-Myer Squibb through <\/p>\n

    ARISTA-USA (BMS\/Pfizer American Thrombosis <\/p>\n

    Investigator Initiated Research Program). The <\/p>\n

    support granted by commercial companies was <\/p>\n

    not used in support of the current study. There are <\/p>\n

    no patents, products in development, or marketed <\/p>\n

    products to declare. <\/p>\n

    Abbreviations: AUC, area under the curve; BCS, <\/p>\n

    bone\/connective tissue\/skin; CCI, Charlson <\/p>\n

    Comorbidity Index; CP, cancer patient; ED, <\/p>\n

    emergency department; HCED, Harris County <\/p>\n

    database; ICD-9 and ICD-9-CM, International <\/p>\n

    Classification of Diseases, 9th Revision, Clinical <\/p>\n

    Modification; LOV, length of visit; MDACC, The <\/p>\n

    University of Texas MD Anderson Cancer Center <\/p>\n

    database; NCP, non-cancer patient; NHAMCS, <\/p>\n

    National Hospital Ambulatory Medical Care Survey <\/p>\n

    database; ROC, receiver-operating characteristic.<\/p>\n<\/p>\n

    https:\/\/doi.org\/10.1371\/journal.pone.0191658 <\/div>\n<\/p>\n<\/div>\n
    \n

    \u201cMDACC\u201d) were obtained from the institution\u2019s tumor registry and electronic medical <\/p>\n

    records. <\/p>\n

    Countywide data were collected from 24 general hospital EDs located in Harris County <\/p>\n

    (hereafter, \u201cHCED\u201d). Harris County had an estimated 4.25 million residents in 2012 [16]. A <\/p>\n

    partnership among the Harris County Hospital District, The University of Texas School of <\/p>\n

    Public Health, and Gateway to Care, established to monitor ED use in the Houston 911 service <\/p>\n

    area [17], provided data from approximately two thirds of the hospital-based ERs within this <\/p>\n

    region. This database contains up to ten International Classification of Diseases, 9th Revision, <\/p>\n

    Clinical Modification (ICD-9) codes per visit. <\/p>\n

    NHAMCS includes a retrospective national probability sample survey of visits to hospital <\/p>\n

    outpatient clinics and EDs in 50 states and the District of Columbia [18]. The Emergency <\/p>\n

    Department Summary uses a manually extracted sample to estimate national ED data. <\/p>\n

    All three databases had basic demographic and clinical information for every ED visit <\/p>\n

    patient, including age, sex, race, cancer type, disposition (admitted, discharged, died, or <\/p>\n

    other), dates and times related to ED visit, insurance (private, government-paid, other\/ <\/p>\n

    unknown), and method of arrival at ED (ambulance, clinic visit, walk). Residence ZIP code <\/p>\n

    was available in the MDACC and HCED databases. <\/p>\n

    Statistical analysis <\/p>\n

    All statistical analyses were performed using R software (version 3.2.2, The R Foundation, <\/p>\n

    http:\/\/www.r-project.org). <\/p>\n

    Data from the time period between January 1, 2007 and December 31, 2009 were analyzed. <\/p>\n

    We used two different methods to define CPs: for MDACC, we examined the institutional <\/p>\n

    tumor registry to determine whether a patient had cancer and, if so, what kind of cancer they <\/p>\n

    had. For HCED and NHAMCS, CPs were determined by association with ICD-9 codes for <\/p>\n

    malignancy, as described by Mayer et al [6]. This method was also applied to the MDACC <\/p>\n

    data, to compare the performance of these two methods and identify potential limitations. <\/p>\n

    All ICD-9 codes for ED visitors were divided among the standard 19 ICD-9 categories, and <\/p>\n

    the percentage frequencies of these code categories were summarized for ED visits and admis- <\/p>\n

    sions through EDs. <\/p>\n

    The Charlson Comorbidity Index (CCI) is a scoring system that is widely used to evaluate <\/p>\n

    the comorbid conditions for prognostic purposes [19]. We calculated the CCI using available <\/p>\n

    ICD-9 codes and the \u201cicd9Charlson\u201d function of the R package \u201cicd9\u201d (version 1.3.1). <\/p>\n

    Random forests is an ensemble learning method for classification, regression, and other <\/p>\n

    tasks that constructs multiple decision trees at training time and outputs the class that is the <\/p>\n

    mode of the classes (classification) or mean prediction (regression) of individual trees. Types <\/p>\n

    of cancer, ICD-9 code categories, important symptoms, and unusual but emergent symptoms <\/p>\n

    were chosen as factors for random-forest analysis to evaluate their importance in the hospitali- <\/p>\n

    zation decision, controlled for demographics (eg, age, sex, race) and ED visit characteristics <\/p>\n

    (eg, arrival by ambulance, visit during business days\/business hours, length of stay). Two ran- <\/p>\n

    dom-forest implementations in R were used: \u201crandomForest\u201d (version 4.6\u201312) and \u201ch2o.ran- <\/p>\n

    domForest\u201d (h2o version 3.10.0.8). Internal validation of the prediction by each random-forest <\/p>\n

    model was performed by randomly dividing each data set into 75% for training and 25% for <\/p>\n

    validation; the performance of each model was assessed by a receiver-operating characteristic <\/p>\n

    (ROC) curve and its area under the curve (AUC). Ranked lists of relative importance of top <\/p>\n

    contributing factors from randomForest and h2o.randomForest were then combined by rank <\/p>\n

    aggregation (R package \u201cRankAggreg\u201d, version 0.5) to assess the association of those factors <\/p>\n

    with hospitalization through the ED. <\/p>\n

    Factors for admission for oncology emergencies <\/p>\n

    PLOS ONE | https:\/\/doi.org\/10.1371\/journal.pone.0191658 February 20, 2018 3 \/ 14<\/p>\n<\/p>\n

    http:\/\/www.r-project.org <\/div>\n
    https:\/\/en.wikipedia.org\/wiki\/Ensemble_learning <\/div>\n
    https:\/\/en.wikipedia.org\/wiki\/Statistical_classification <\/div>\n
    https:\/\/en.wikipedia.org\/wiki\/Regression_analysis <\/div>\n
    https:\/\/en.wikipedia.org\/wiki\/Decision_tree_learning <\/div>\n
    https:\/\/en.wikipedia.org\/wiki\/Mode_(statistics <\/div>\n
    https:\/\/doi.org\/10.1371\/journal.pone.0191658 <\/div>\n<\/p>\n<\/div>\n
    \n

    Results <\/p>\n

    Patient characteristics <\/p>\n

    We identified ~3.4 million ED visits between 2007 and 2009. In the MDACC database, <\/p>\n

    there were 47,245 ED visits, including 44,143 visits by CPs [93.4%] and 3,102 visits by <\/p>\n

    NCPs per the tumor registry, or 32,477 visits by CPs and 14,768 visits by NCPs per ICD-9. <\/p>\n

    In the HCED database, there were 3,248,973 ED visits (44,583 CPs [1.4%] and 3,204,390 <\/p>\n

    NCPs); in the NHAMCS database, there were 104,566 ED visits (632 CPs [0.6%] and <\/p>\n

    103,934 NCPs). <\/p>\n

    In the MDACC database, 17,673 ED visitors were hospitalized (17,238 CPs, 435 NCPs per <\/p>\n

    tumor registry, or 12,691 CPs, 4,982 NCPs per ICD-9); in the HCED database, 153,782 were <\/p>\n

    hospitalized (8,570 CPs, 145,212 NCPs); and in the NHAMCS database, 14,428 were hospital- <\/p>\n

    ized (301 CPs, 14,127 NCPs) (Fig 1). CPs as defined by the tumor registry accounted for nearly <\/p>\n

    95% of patients visiting the MD Anderson ED, but CPs comprised only 1% of patients visiting <\/p>\n

    the general EDs. A higher hospitalization rate was found for CPs than for NCPs in each data- <\/p>\n

    base (MDACC: 39.1% vs 14.0% per tumor registry; HCED: 19.2% vs 4.5%; NHAMCS: 47.6% <\/p>\n

    vs 13.6%; P<0.01). <\/p>\n

    Fewer CPs with hematological malignancies (leukemia, lymphoma\/myeloma) visited gen- <\/p>\n

    eral EDs than visited the MD Anderson ED (Fig 2). Lung, gastrointestinal (excluding colorec- <\/p>\n

    tal), and genitourinary (excluding prostate) cancers were the three most common cancer <\/p>\n

    diagnoses related to ED visits at general EDs, apart from the miscellaneous category \u201cother <\/p>\n

    cancers\u201d (several rare cancers and metastatic cancer with an unknown primary tumor). <\/p>\n

    Among hospitalized CPs, leukemia, lymphoma\/myeloma, and lung cancer were the three <\/p>\n

    most common cancer diagnoses related to ED visits in MDACC, whereas lung and gastrointes- <\/p>\n

    tinal (excluding colorectal) cancer were the most common cancer diagnoses related to ED vis- <\/p>\n

    its in HCED and NHAMCS. For all cancer types, CP admission rates in HCED were the <\/p>\n

    lowest among the three data sets (S1 Fig). The admission rates in NHAMCS were higher than <\/p>\n

    those in MDACC for all cancer types except colorectal cancer, leukemia, cancer of the lip\/oral <\/p>\n

    cavity\/pharynx, lymphoma\/myeloma, and cancer of the respiratory system not including lung <\/p>\n

    cancer. <\/p>\n

    Age <\/p>\n

    In the pooled data from all three data sets, CPs visiting the ED were older than NCPs (CPs: <\/p>\n

    57.87\u00b118.47 years; NCPs: 33.16\u00b124.12 years; P<0.01); the same was true for admitted patients <\/p>\n

    (CPs: 58.61\u00b117.73 years; NCPs: 50.84\u00b126.18 years; P<0.01). After defining seven age groups, <\/p>\n

    one for every 15 years of life, differences between CPs and NCPs in the percentage distribu- <\/p>\n

    tions of ED visits and admissions through EDs became apparent in all three databases (S2 Fig). <\/p>\n

    NCP children and young adults were the most common ED visitors in HCED and NHAMCS. <\/p>\n

    Because most NCP ED visitors in the MDACC database were employees, visitors, and family <\/p>\n

    and friends of CPs being treated at MD Anderson, the percentage distribution was very low <\/p>\n

    for the pediatric group and peaked at 46\u201360-years of age. Among CPs and NCPs admitted <\/p>\n

    through the ED, most were 46\u201375 years of age. <\/p>\n

    CPs and NCPs visiting the ED had various ICD-9 diagnoses across age ranges (Fig 3). For <\/p>\n

    example, apart from visiting the ED for symptoms and cancer diagnoses, CPs aged 50\u201375 <\/p>\n

    years visited the ED for endocrine\/metabolic, circulatory, respiratory, and gastrointestinal dis- <\/p>\n

    ease. However, NCPs aged 50\u201375 years visited the ED mainly for endocrine\/metabolic and cir- <\/p>\n

    culatory disease, in addition to symptoms. <\/p>\n

    Factors for admission for oncology emergencies <\/p>\n

    PLOS ONE | https:\/\/doi.org\/10.1371\/journal.pone.0191658 February 20, 2018 4 \/ 14<\/p>\n<\/p>\n

    https:\/\/doi.org\/10.1371\/journal.pone.0191658 <\/div>\n<\/p>\n<\/div>\n
    \n

    Residence and insurance <\/p>\n

    MD Anderson is a comprehensive cancer center with a national referral base; several major <\/p>\n

    hospitals in the Texas Medical Center are also major tertiary referral centers for a variety of <\/p>\n

    nonmalignant diseases. As residence ZIP codes were available in the MDACC and HCED <\/p>\n

    databases, we used that data to visualize and compare the relationships between geographic <\/p>\n

    Fig 1. Numbers of visitors who were discharged, hospitalized, or died in ED between 2007 and 2009. (A, B) MDACC. (C) HCED. (D) NHAMCS. CPs were <\/p>\n

    discovered by association with ICD-9 codes for malignancies (A, C, D) or by a tumor registry (B). <\/p>\n

    https:\/\/doi.org\/10.1371\/journal.pone.0191658.g001 <\/p>\n

    Factors for admission for oncology emergencies <\/p>\n

    PLOS ONE | https:\/\/doi.org\/10.1371\/journal.pone.0191658 February 20, 2018 5 \/ 14<\/p>\n<\/p>\n

    https:\/\/doi.org\/10.1371\/journal.pone.0191658.g001 <\/div>\n
    https:\/\/doi.org\/10.1371\/journal.pone.0191658 <\/div>\n<\/p>\n<\/div>\n
    \n

    location of residence and insurance type for the patients who visited EDs and those who were <\/p>\n

    admitted (Fig 4). <\/p>\n

    Most of the MDACC ED visitors were CPs from various parts of the United States and had <\/p>\n

    private insurance, whereas the MDACC NCPs were mainly from Harris County and its vicin- <\/p>\n

    ity and were covered by government insurance (Fig 4A). In contrast, most of the HCED ED <\/p>\n

    visitors were NCPs from various parts of the United States, while the HCED CPs were mainly <\/p>\n

    from Harris County and its vicinity. The percentages of private insurance and government <\/p>\n

    insurance were equal in HCED ED visitors overall (both CPs and NCPs) (Fig 4B). For both the <\/p>\n

    MDACC CPs and NCPs admitted to the hospital, the patterns were similar to those seen in ED <\/p>\n

    visitors (Fig 4C). For the HCED admitted patients, most admitted patients were from Harris <\/p>\n

    County and its vicinity and were covered by government insurance (Fig 4D). <\/p>\n

    Time <\/p>\n

    All the ED visits and admissions via ED in all three databases were examined for variations by <\/p>\n

    time of the day, day of the week, day of the month, and month of the year. The time of the day <\/p>\n

    for ED visits and admissions ranged from the fewest visits and admissions in the early morning <\/p>\n

    hours (4 am to 7 am) to peak numbers in midafternoon (12 pm to 3 pm) for both CPs and <\/p>\n

    NCPs (S3 Fig, upper panels). As for the day of the week (S3 Fig, lower panels), a decrease in <\/p>\n

    visits and admissions from Monday to Sunday was observed for CPs (with the lowest numbers <\/p>\n

    on Saturday); however, no similar trend was seen for NCPs. Moreover, compared with NCPs, <\/p>\n

    CPs had longer ED stays (CPs: 11.55\u00b110.22 hours; NCPs: 6.03\u00b17.93 hours: P<0.001] in all <\/p>\n

    Fig 2. Percentages of various cancer types in ED visitors and those who got admission. Percentages of various cancer types in patients who visited the ED (top row) <\/p>\n

    and in those who were admitted through the ED (bottom row). The thickness of each pie is scaled to represent the total number of CPs in each data set. <\/p>\n

    https:\/\/doi.org\/10.1371\/journal.pone.0191658.g002 <\/p>\n

    Factors for admission for oncology emergencies <\/p>\n

    PLOS ONE | https:\/\/doi.org\/10.1371\/journal.pone.0191658 February 20, 2018 6 \/ 14<\/p>\n<\/p>\n

    https:\/\/doi.org\/10.1371\/journal.pone.0191658.g002 <\/div>\n
    https:\/\/doi.org\/10.1371\/journal.pone.0191658 <\/div>\n<\/p>\n<\/div>\n
    \n

    three databases, indicating that the severity of illness in CPs was greater than that in NCPs and <\/p>\n

    that more medical resources were consumed by CPs. <\/p>\n

    Factors associated with admission through EDs in CPs and NCPs <\/p>\n

    Among patients admitted through EDs, CPs generally had higher admission rates than NCPs <\/p>\n

    across the large majority of diagnostic groups (S4 Fig). The admission rates of CPs in MDACC <\/p>\n

    agree with those in NHAMCS for most diagnostic groups. <\/p>\n

    Random forest methodology was used to identify important factors associated with the <\/p>\n

    decision to admit for all ED visitors. After optimizing the numbers of trees in the random for- <\/p>\n

    est analysis, we ran the \u201crandomForest\u201d R package to determine appropriate cutpoints for age, <\/p>\n

    length of stay in ED, and comorbidities (CCI) in CPs and NCPs. As already shown in S2 Fig, <\/p>\n

    the influence of age on admission was different between CPs and NCPs. Since the admission <\/p>\n

    Fig 3. Heat maps of ICD-9 codes at different ages for CPs and NCPs. The color key shown to the right of each panel relates color intensity to the number of patients. <\/p>\n

    https:\/\/doi.org\/10.1371\/journal.pone.0191658.g003 <\/p>\n

    Factors for admission for oncology emergencies <\/p>\n

    PLOS ONE | https:\/\/doi.org\/10.1371\/journal.pone.0191658 February 20, 2018 7 \/ 14<\/p>\n<\/p>\n

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