Read the article,?Oncologic emergencies in a cancer center emergency department

 

Instructions

Read the article, “Oncologic emergencies in a cancer center emergency department and in general emergency departments countywide and nationwide”.  You may access the article in the resource folder. 

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.

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. 

Please make sure to provide citations and references (in APA, 7th ed. format) for your work.  

Case Study Focus:

Based on your comprehensive reading, create a Case study highlighting key strategies for effectively managing oncologic emergencies in critically ill patients.

Key Points

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.

Timely Diagnosis: Emphasize the importance of timely diagnosis and intervention. Provide examples from the article illustrating the benefits of early recognition and swift treatment.

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

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

Risk Stratification: Explain the role of risk assessment in identifying high-risk patients and the significance of early interventions to prevent complications.

Education and Training: Address the impact of education and training on enhancing oncologic emergencies management. Highlight the benefits of well informed healthcare professionals.

Conclusion: Summarize key insights from the article and emphasize the patient centered approach to managing oncologic emergencies in critically ill patients.

  • Oncologic_emergenciespdf..pdf

RESEARCH ARTICLE

Oncologic emergencies in a cancer center

emergency department and in general

emergency departments countywide and

nationwide

Zhi Yang1¤a, Runxiang Yang1¤b, Min Ji Kwak1¤c, Aiham Qdaisat1, Junzhong Lin1¤d, Charles

E. Begley2, Cielito C. Reyes-Gibby1, Sai-Ching Jim Yeung1,3*

1 Department of Emergency Medicine, The University of Texas MD Anderson Cancer Center, Houston,

Texas, United States of America, 2 Division of Management, Policy, and Community Health, The University

of Texas Health Science Center at Houston School of Public Health, Houston, Texas, United States of

America, 3 Department of Endocrine Neoplasia and Hormonal Disorders, The University of Texas MD

Anderson Cancer Center, Houston, Texas, United States of America

¤a Current address: Department of Intensive Care, Guangzhou First People’s Hospital, Guangzhou Medical

University, Guangzhou, Guangdong, People’s Republic of China

¤b Current address: Second Department of Medical Oncology, Tumor Hospital of Yunnan Province,

Kunming, Yunnan, People’s Republic of China

¤c Current address: Department of Medicine, The University of Texas Health Science Center, Houston,

Texas, United States of America

¤d Current address: Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, Guangzhou,

Guangdong, People’s Republic of China

* [email protected]

Abstract

Background

Although cancer patients (CPs) are increasingly likely to visit emergency department (ED),

no population-based study has compared the characteristics of CPs and non-cancer

patients (NCPs) who visit the ED and examined factors associated with hospitalization via

the ED. In this study, we (1) compared characteristics and diagnoses between CPs and

NCPs who visited the ED in a cancer center or general hospital; (2) compared characteris-

tics and diagnoses between CPs and NCPs who were hospitalized via the ED in a cancer

center or general hospital; and (3) investigated important factors associated with such

hospitalization.

Methods and findings

We analyzed patient characteristic and diagnosis [based on International Classification of

Diseases-9 (ICD-9) codes] data from the ED of a comprehensive cancer center (MDACC),

24 general EDs in Harris County, Texas (HCED), and the National Hospital Ambulatory

Medical Care Survey (NHAMCS) from 1/1/2007–12/31/2009. Approximately 3.4 million ED

visits were analyzed: 47,245, 3,248,973, and 104,566 visits for MDACC, HCED, and

NHAMCS, respectively, of which 44,143 (93.4%), 44,583 (1.4%), and 632 (0.6%) were CP

visits. CPs were older than NCPs and stayed longer in EDs. Lung, gastrointestinal

PLOS ONE | https://doi.org/10.1371/journal.pone.0191658 February 20, 2018 1 / 14

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OPENACCESS

Citation: Yang Z, Yang R, Kwak MJ, Qdaisat A, Lin

J, Begley CE, et al. (2018) Oncologic emergencies

in a cancer center emergency department and in

general emergency departments countywide and

nationwide. PLoS ONE 13(2): e0191658. https://

doi.org/10.1371/journal.pone.0191658

Editor: Luis Costa, Hospital de Santa Maria,

PORTUGAL

Received: July 10, 2017

Accepted: January 9, 2018

Published: February 20, 2018

Copyright: This is an open access article, free of all

copyright, and may be freely reproduced,

distributed, transmitted, modified, built upon, or

otherwise used by anyone for any lawful purpose.

The work is made available under the Creative

Commons CC0 public domain dedication.

Data Availability Statement: All relevant data are

within the paper and its Supporting Information

files.

Funding: ZY is supported by Guangzhou First

People’s Hospital, Guangzhou Medical University.

RY is partially supported by the National Natural

Science Foundation of China (81360393 and

81560432). JL is supported by Sun Yat-sen

University Cancer Center. CRG is the Principal

Investigator of and is supported by the Program in

Oncologic Emergency Medicine of The University

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(excluding colorectal), and genitourinary (excluding prostate) cancers were the three most

common diagnoses related to ED visits at general EDs. CPs visiting MDACC were more

likely than CPs visiting HCED to be privately insured. CPs were more likely than NCPs to be

hospitalized. Pneumonia and influenza, fluid and electrolyte disorders, and fever were

important predictive factors for CP hospitalization; coronary artery disease, cerebrovascular

disease, and heart failure were important factors for NCP hospitalization.

Conclusions

CPs consumed more ED resources than NCPs and had a higher hospitalization rate. Given

the differences in characteristics and diagnoses between CPs and NCPs, ED physicians

must pay special attention to CPs and be familiar with their unique set of oncologic

emergencies.

Introduction

Given the increasing incidence of and declining mortality rate for cancer worldwide, cancer

patients (CPs) are increasingly likely to visit an emergency department (ED), either in cancer

centers or general hospitals, at least once to obtain urgent care [1–3]. In previous studies, the

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

(11.9%) [5]. Moreover, as CPs have unique sequelae related to their disease and treatment, it is

crucial for both general and cancer-specialist ED physicians to better understand the needs of

CPs in emergent situations.

Research on CP ED visits has focused primarily on cancer type and chief complaints [1, 6,

7] end-of-life ED visits [3, 8, 9] or specific cancer types [10–12]. Most of this research has

focused on commonalities among CPs; to our knowledge, none has compared the characteris-

tics of CPs and NCPs who visit the ED, either in cancer centers or general hospitals. Moreover,

although several studies have shown that hospitalization via the ED is a clinically important

marker of poorer prognosis for CPs [13–15], no population-based study has examined factors

associated with CP hospitalization via the ED.

In this study, we (1) compared characteristics and diagnoses between CPs and NCPs who

visited the ED in a cancer center or general hospital; (2) compared characteristics and diagno-

ses between CPs and NCPs who were hospitalized via the ED in a cancer center or general hos-

pital; and (3) investigated important factors associated with such hospitalization.

Methods

Data collection

We collected data on the characteristics of visitors to the ED at The University of Texas MD

Anderson Cancer Center in Houston, Texas, visitors to EDs at general hospitals in Harris

County, Texas (which includes Houston), and ED visitors assessed in the US National Hospital

Ambulatory Medical Care Survey (NHAMCS). Our study was conducted under a clinical

research protocol (DR08-0066) approved by the MD Anderson Institutional Review Board

and in compliance with Health Insurance Portability and Accountability Act regulations. As

this was a retrospective data review, informed consent requirements were waived.

MD Anderson is a specialized referral center for cancer care. Its ED handles ~22,000 patient

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

Factors for admission for oncology emergencies

PLOS ONE | https://doi.org/10.1371/journal.pone.0191658 February 20, 2018 2 / 14

of Texas MD Anderson Cancer Center. The

University of Texas MD Anderson Cancer Center is

supported in part by the National Institutes of

Health through Cancer Center Support Grant P30

CA016672. The funders had no role in study

design, data collection and analysis, decision to

publish, or preparation of the manuscript.

Competing interests: Dr. Yeung is the principal

investigator of an investigator-initiated clinical trial

supported by DepoMed and a retrospective clinical

study supported by Bristol-Myer Squibb through

ARISTA-USA (BMS/Pfizer American Thrombosis

Investigator Initiated Research Program). The

support granted by commercial companies was

not used in support of the current study. There are

no patents, products in development, or marketed

products to declare.

Abbreviations: AUC, area under the curve; BCS,

bone/connective tissue/skin; CCI, Charlson

Comorbidity Index; CP, cancer patient; ED,

emergency department; HCED, Harris County

database; ICD-9 and ICD-9-CM, International

Classification of Diseases, 9th Revision, Clinical

Modification; LOV, length of visit; MDACC, The

University of Texas MD Anderson Cancer Center

database; NCP, non-cancer patient; NHAMCS,

National Hospital Ambulatory Medical Care Survey

database; ROC, receiver-operating characteristic.

https://doi.org/10.1371/journal.pone.0191658

“MDACC”) were obtained from the institution’s tumor registry and electronic medical

records.

Countywide data were collected from 24 general hospital EDs located in Harris County

(hereafter, “HCED”). Harris County had an estimated 4.25 million residents in 2012 [16]. A

partnership among the Harris County Hospital District, The University of Texas School of

Public Health, and Gateway to Care, established to monitor ED use in the Houston 911 service

area [17], provided data from approximately two thirds of the hospital-based ERs within this

region. This database contains up to ten International Classification of Diseases, 9th Revision,

Clinical Modification (ICD-9) codes per visit.

NHAMCS includes a retrospective national probability sample survey of visits to hospital

outpatient clinics and EDs in 50 states and the District of Columbia [18]. The Emergency

Department Summary uses a manually extracted sample to estimate national ED data.

All three databases had basic demographic and clinical information for every ED visit

patient, including age, sex, race, cancer type, disposition (admitted, discharged, died, or

other), dates and times related to ED visit, insurance (private, government-paid, other/

unknown), and method of arrival at ED (ambulance, clinic visit, walk). Residence ZIP code

was available in the MDACC and HCED databases.

Statistical analysis

All statistical analyses were performed using R software (version 3.2.2, The R Foundation,

http://www.r-project.org).

Data from the time period between January 1, 2007 and December 31, 2009 were analyzed.

We used two different methods to define CPs: for MDACC, we examined the institutional

tumor registry to determine whether a patient had cancer and, if so, what kind of cancer they

had. For HCED and NHAMCS, CPs were determined by association with ICD-9 codes for

malignancy, as described by Mayer et al [6]. This method was also applied to the MDACC

data, to compare the performance of these two methods and identify potential limitations.

All ICD-9 codes for ED visitors were divided among the standard 19 ICD-9 categories, and

the percentage frequencies of these code categories were summarized for ED visits and admis-

sions through EDs.

The Charlson Comorbidity Index (CCI) is a scoring system that is widely used to evaluate

the comorbid conditions for prognostic purposes [19]. We calculated the CCI using available

ICD-9 codes and the “icd9Charlson” function of the R package “icd9” (version 1.3.1).

Random forests is an ensemble learning method for classification, regression, and other

tasks that constructs multiple decision trees at training time and outputs the class that is the

mode of the classes (classification) or mean prediction (regression) of individual trees. Types

of cancer, ICD-9 code categories, important symptoms, and unusual but emergent symptoms

were chosen as factors for random-forest analysis to evaluate their importance in the hospitali-

zation decision, controlled for demographics (eg, age, sex, race) and ED visit characteristics

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

dom-forest implementations in R were used: “randomForest” (version 4.6–12) and “h2o.ran-

domForest” (h2o version 3.10.0.8). Internal validation of the prediction by each random-forest

model was performed by randomly dividing each data set into 75% for training and 25% for

validation; the performance of each model was assessed by a receiver-operating characteristic

(ROC) curve and its area under the curve (AUC). Ranked lists of relative importance of top

contributing factors from randomForest and h2o.randomForest were then combined by rank

aggregation (R package “RankAggreg”, version 0.5) to assess the association of those factors

with hospitalization through the ED.

Factors for admission for oncology emergencies

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

Results

Patient characteristics

We identified ~3.4 million ED visits between 2007 and 2009. In the MDACC database,

there were 47,245 ED visits, including 44,143 visits by CPs [93.4%] and 3,102 visits by

NCPs per the tumor registry, or 32,477 visits by CPs and 14,768 visits by NCPs per ICD-9.

In the HCED database, there were 3,248,973 ED visits (44,583 CPs [1.4%] and 3,204,390

NCPs); in the NHAMCS database, there were 104,566 ED visits (632 CPs [0.6%] and

103,934 NCPs).

In the MDACC database, 17,673 ED visitors were hospitalized (17,238 CPs, 435 NCPs per

tumor registry, or 12,691 CPs, 4,982 NCPs per ICD-9); in the HCED database, 153,782 were

hospitalized (8,570 CPs, 145,212 NCPs); and in the NHAMCS database, 14,428 were hospital-

ized (301 CPs, 14,127 NCPs) (Fig 1). CPs as defined by the tumor registry accounted for nearly

95% of patients visiting the MD Anderson ED, but CPs comprised only 1% of patients visiting

the general EDs. A higher hospitalization rate was found for CPs than for NCPs in each data-

base (MDACC: 39.1% vs 14.0% per tumor registry; HCED: 19.2% vs 4.5%; NHAMCS: 47.6%

vs 13.6%; P<0.01).

Fewer CPs with hematological malignancies (leukemia, lymphoma/myeloma) visited gen-

eral EDs than visited the MD Anderson ED (Fig 2). Lung, gastrointestinal (excluding colorec-

tal), and genitourinary (excluding prostate) cancers were the three most common cancer

diagnoses related to ED visits at general EDs, apart from the miscellaneous category “other

cancers” (several rare cancers and metastatic cancer with an unknown primary tumor).

Among hospitalized CPs, leukemia, lymphoma/myeloma, and lung cancer were the three

most common cancer diagnoses related to ED visits in MDACC, whereas lung and gastrointes-

tinal (excluding colorectal) cancer were the most common cancer diagnoses related to ED vis-

its in HCED and NHAMCS. For all cancer types, CP admission rates in HCED were the

lowest among the three data sets (S1 Fig). The admission rates in NHAMCS were higher than

those in MDACC for all cancer types except colorectal cancer, leukemia, cancer of the lip/oral

cavity/pharynx, lymphoma/myeloma, and cancer of the respiratory system not including lung

cancer.

Age

In the pooled data from all three data sets, CPs visiting the ED were older than NCPs (CPs:

57.87±18.47 years; NCPs: 33.16±24.12 years; P<0.01); the same was true for admitted patients

(CPs: 58.61±17.73 years; NCPs: 50.84±26.18 years; P<0.01). After defining seven age groups,

one for every 15 years of life, differences between CPs and NCPs in the percentage distribu-

tions of ED visits and admissions through EDs became apparent in all three databases (S2 Fig).

NCP children and young adults were the most common ED visitors in HCED and NHAMCS.

Because most NCP ED visitors in the MDACC database were employees, visitors, and family

and friends of CPs being treated at MD Anderson, the percentage distribution was very low

for the pediatric group and peaked at 46–60-years of age. Among CPs and NCPs admitted

through the ED, most were 46–75 years of age.

CPs and NCPs visiting the ED had various ICD-9 diagnoses across age ranges (Fig 3). For

example, apart from visiting the ED for symptoms and cancer diagnoses, CPs aged 50–75

years visited the ED for endocrine/metabolic, circulatory, respiratory, and gastrointestinal dis-

ease. However, NCPs aged 50–75 years visited the ED mainly for endocrine/metabolic and cir-

culatory disease, in addition to symptoms.

Factors for admission for oncology emergencies

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https://doi.org/10.1371/journal.pone.0191658

Residence and insurance

MD Anderson is a comprehensive cancer center with a national referral base; several major

hospitals in the Texas Medical Center are also major tertiary referral centers for a variety of

nonmalignant diseases. As residence ZIP codes were available in the MDACC and HCED

databases, we used that data to visualize and compare the relationships between geographic

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

discovered by association with ICD-9 codes for malignancies (A, C, D) or by a tumor registry (B).

https://doi.org/10.1371/journal.pone.0191658.g001

Factors for admission for oncology emergencies

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https://doi.org/10.1371/journal.pone.0191658.g001
https://doi.org/10.1371/journal.pone.0191658

location of residence and insurance type for the patients who visited EDs and those who were

admitted (Fig 4).

Most of the MDACC ED visitors were CPs from various parts of the United States and had

private insurance, whereas the MDACC NCPs were mainly from Harris County and its vicin-

ity and were covered by government insurance (Fig 4A). In contrast, most of the HCED ED

visitors were NCPs from various parts of the United States, while the HCED CPs were mainly

from Harris County and its vicinity. The percentages of private insurance and government

insurance were equal in HCED ED visitors overall (both CPs and NCPs) (Fig 4B). For both the

MDACC CPs and NCPs admitted to the hospital, the patterns were similar to those seen in ED

visitors (Fig 4C). For the HCED admitted patients, most admitted patients were from Harris

County and its vicinity and were covered by government insurance (Fig 4D).

Time

All the ED visits and admissions via ED in all three databases were examined for variations by

time of the day, day of the week, day of the month, and month of the year. The time of the day

for ED visits and admissions ranged from the fewest visits and admissions in the early morning

hours (4 am to 7 am) to peak numbers in midafternoon (12 pm to 3 pm) for both CPs and

NCPs (S3 Fig, upper panels). As for the day of the week (S3 Fig, lower panels), a decrease in

visits and admissions from Monday to Sunday was observed for CPs (with the lowest numbers

on Saturday); however, no similar trend was seen for NCPs. Moreover, compared with NCPs,

CPs had longer ED stays (CPs: 11.55±10.22 hours; NCPs: 6.03±7.93 hours: P<0.001] in all

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)

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.

https://doi.org/10.1371/journal.pone.0191658.g002

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https://doi.org/10.1371/journal.pone.0191658.g002
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three databases, indicating that the severity of illness in CPs was greater than that in NCPs and

that more medical resources were consumed by CPs.

Factors associated with admission through EDs in CPs and NCPs

Among patients admitted through EDs, CPs generally had higher admission rates than NCPs

across the large majority of diagnostic groups (S4 Fig). The admission rates of CPs in MDACC

agree with those in NHAMCS for most diagnostic groups.

Random forest methodology was used to identify important factors associated with the

decision to admit for all ED visitors. After optimizing the numbers of trees in the random for-

est analysis, we ran the “randomForest” R package to determine appropriate cutpoints for age,

length of stay in ED, and comorbidities (CCI) in CPs and NCPs. As already shown in S2 Fig,

the influence of age on admission was different between CPs and NCPs. Since the admission

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.

https://doi.org/10.1371/journal.pone.0191658.g003

Factors for admission for oncology emergencies

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