Acute and Critical Care

Original Articles

Pulmonary
Clinical characteristics and outcomes of critically Ill patients with COVID-19 in Northeast Ohio: low mortality and length of stay: Francois Abi Fadel, Mohammed Al-Jaghbeer, Sany Kumar, Lori Griffiths, Xiaofeng Wang, Xiaozhen Han, Robert Burton; Acute Crit Care. 2020;35(4):242-248. Published online October 12, 2020; DOI: https://doi.org/10.4266/acc.2020.00619

6,053 View
275 Download
9 Web of Science
9 Crossref

Background
Published coronavirus disease 2019 (COVID-19) reports suggest higher mortality with increasing age and comorbidities. Our study describes the clinical characteristics and outcomes for all intensive care unit (ICU) patients admitted across the Cleveland Clinic enterprise, a 10-hospital health care system in Northeast Ohio, serving more than 2.7 million people.
Methods
We analyzed the quality data registry for clinical characteristics and outcomes of all COVID-19-confirmed ICU admissions. Differences in outcomes from other health care systems and published cohorts from other parts of the world were delineated.
Results
Across our health care system, 495 COVID-19 patients were admitted from March 15 to June 1, 2020. Mean patient age was 67.3 years, 206 (41.6%) were females, and 289 (58.4%) were males. Mean Acute Physiology Score was 45.3, and mean Acute Physiology and Chronic Health Evaluation III score was 60.5. In total, 215 patients (43.3%) were intubated for a mean duration of 9.2 days. Mean ICU and hospital length of stay were 7.4 and 13.9 days, respectively, while mean ICU and hospital mortality rates were 18.4% and 23.8%.
Conclusions
Our health care system cohort is the fourth largest to be reported. Lower ICU and hospital mortality and length of stay were seen compared to most other published reports. Better preparedness and state-level control of the surge in COVID-19 infections are likely the reasons for these better outcomes. Future research is needed to further delineate differences in mortality and length of stay across health care systems and over time.

Citations

Citations to this article as recorded by

Racial inequality in COVID-treatment and in-hospital length of stay in the US over time
Benjamin M. Althouse, Charlotte Baker, Peter D. Smits, Samuel Gratzl, Ryan H. Lee, Brianna M. Goodwin Cartwright, Michael Simonov, Michael D. Wang, Nicholas L. Stucky
Frontiers in Public Health.2023;[Epub] CrossRef
Effects of race on the outcome of COVID-19 in hospitalized patients
Getahun Abate, Aniruddh Kapoor, Edward Charbek, Bryan Beck, Qian Wang, Grace C. Wang, Mackenzie Steck, Jason Zoglman, Robin R. Chambeg, Sharon Frey, Daniel F. Hoft, Timothy L. Wiemken
Journal of the National Medical Association.2022; 114(1): 56. CrossRef
Hospital length of stay for COVID-19 patients: A systematic review and meta-analysis
Yousef Alimohamadi, Elahe Mansouri Yekta, Mojtaba Sepandi, Maedeh Sharafoddin, Maedeh Arshadi, Elahe Hesari
Multidisciplinary Respiratory Medicine.2022;[Epub] CrossRef
Learning from the First Wave of the Pandemic in England, Wales, and Northern Ireland
David Pilcher, Matthew Durie
American Journal of Respiratory and Critical Care Medicine.2021; 203(5): 532. CrossRef
Epidemiology, outcomes, and utilization of intensive care unit resources for critically ill COVID-19 patients in Libya: A prospective multi-center cohort study
Muhammed Elhadi, Ahmed Alsoufi, Abdurraouf Abusalama, Akram Alkaseek, Saedah Abdeewi, Mohammed Yahya, Alsnosy Mohammed, Mohammed Abdelkabir, Mohammed Huwaysh, Emad Amkhatirah, Kamel Alshorbaji, Samer Khel, Marwa Gamra, Abdulmueti Alhadi, Taha Abubaker, Mo
PLOS ONE.2021; 16(4): e0251085. CrossRef
A Multicenter Evaluation of Survival After In-Hospital Cardiac Arrest in Coronavirus Disease 2019 Patients
Abhishek Bhardwaj, Mahmoud Alwakeel, Talha Saleem, Saira Afzal, Sura Alqaisi, Aisha R. Saand, Hanan Al. Najjar, Lori Griffiths, Xiaozhen Han, Xiaofeng Wang, Silvia Perez-Protto, Benjamin S. Abella, David F. Gaieski, Abhijit Duggal, Francois Abi Fadel
Critical Care Explorations.2021; 3(5): e0425. CrossRef
Overcoming gaps: regional collaborative to optimize capacity management and predict length of stay of patients admitted with COVID-19
Michael G Usher, Roshan Tourani, Gyorgy Simon, Christopher Tignanelli, Bryan Jarabek, Craig E Strauss, Stephen C Waring, Niall A M Klyn, Burke T Kealey, Rabindra Tambyraja, Deepti Pandita, Karyn D Baum
JAMIA Open.2021;[Epub] CrossRef
Examining the Clinical Prognosis of Critically Ill Patients with COVID-19 Admitted to Intensive Care Units: A Nationwide Saudi Study
Abbas Al Mutair, Alyaa Elhazmi, Saad Alhumaid, Gasmelseed Ahmad, Ali Rabaan, Mohammed Alghadeer, Hiba Chagla, Raghavendra Tirupathi, Amit Sharma, Kuldeep Dhama, Khulud Alsalman, Zainab Alalawi, Ziyad Aljofan, Alya Al Mutairi, Mohammed Alomari, Mansour Awa
Medicina.2021; 57(9): 878. CrossRef
Comparison of characteristics and ventilatory course between coronavirus disease 2019 and Middle East respiratory syndrome patients with acute respiratory distress syndrome
Imran Khalid, Romaysaa M Yamani, Maryam Imran, Muhammad Ali Akhtar, Manahil Imran, Rumaan Gul, Tabindeh Jabeen Khalid, Ghassan Y Wali
Acute and Critical Care.2021; 36(3): 223. CrossRef

Rapid response system
Effectiveness of a daytime rapid response system in hospitalized surgical ward patients: Eunjin Yang, Hannah Lee, Sang-Min Lee, Sulhee Kim, Ho Geol Ryu, Hyun Joo Lee, Jinwoo Lee, Seung-Young Oh; Acute Crit Care. 2020;35(2):77-86. Published online May 13, 2020; DOI: https://doi.org/10.4266/acc.2019.00661

6,141 View
211 Download
6 Web of Science
8 Crossref

Abstract PDF Supplementary Material

Background
Clinical deteriorations during hospitalization are often preventable with a rapid response system (RRS). We aimed to investigate the effectiveness of a daytime RRS for surgical hospitalized patients.
Methods
A retrospective cohort study was conducted in 20 general surgical wards at a 1,779-bed University hospital from August 2013 to July 2017 (August 2013 to July 2015, pre-RRS-period; August 2015 to July 2017, post-RRS-period). The primary outcome was incidence of cardiopulmonary arrest (CPA) when the RRS was operating. The secondary outcomes were the incidence of total and preventable cardiopulmonary arrest, in-hospital mortality, the percentage of “do not resuscitate” orders, and the survival of discharged CPA patients.
Results
The relative risk (RR) of CPA per 1,000 admissions during RRS operational hours (weekdays from 7 AM to 7 PM) in the post-RRS-period compared to the pre-RRS-period was 0.53 (95% confidence interval [CI], 0.25 to 1.13; P=0.099) and the RR of total CPA regardless of RRS operating hours was 0.76 (95% CI, 0.46 to 1.28; P=0.301). The preventable CPA after RRS implementation was significantly lower than that before RRS implementation (RR, 0.31; 95% CI, 0.11 to 0.88; P=0.028). There were no statistical differences in in-hospital mortality and the survival rate of patients with in-hospital cardiac arrest. Do-not-resuscitate decisions significantly increased during after RRS implementation periods compared to pre-RRS periods (RR, 1.91; 95% CI, 1.40 to 2.59; P<0.001).
Conclusions
The day-time implementation of the RRS did not significantly reduce the rate of CPA whereas the system effectively reduced the rate of preventable CPA during periods when the system was operating.

Citations

Citations to this article as recorded by

Development and Validation of a Machine Learning Algorithm Using Clinical Pages to Predict Imminent Clinical Deterioration
Bryan D. Steitz, Allison B. McCoy, Thomas J. Reese, Siru Liu, Liza Weavind, Kipp Shipley, Elise Russo, Adam Wright
Journal of General Internal Medicine.2024; 39(1): 27. CrossRef
Effects of a Rapid Response Team on Patient Outcomes: A Systematic Review
Qiuxia Zhang, Khuan Lee, Zawiah Mansor, Iskasymar Ismail, Yi Guo, Qiao Xiao, Poh Ying Lim
Heart & Lung.2024; 63: 51. CrossRef
Clinical significance of acute care surgery system as a part of hospital medical emergency team for hospitalized patients
Kyoung Won Yoon, Kyoungjin Choi, Keesang Yoo, Eunmi Gil, Chi-Min Park
Annals of Surgical Treatment and Research.2023; 104(1): 43. CrossRef
The associations between rapid response systems and their components with patient outcomes: A scoping review
Rebecca J. Piasecki, Cheryl R. Dennison Himmelfarb, Kelly T. Gleason, Rachel M. Justice, Elizabeth A. Hunt
International Journal of Nursing Studies Advances.2023; 5: 100134. CrossRef
Changes in the incidence of cardiopulmonary resuscitation before and after implementation of the Life-Sustaining Treatment Decisions Act
Hyunjae Im, Hyun Woo Choe, Seung-Young Oh, Ho Geol Ryu, Hannah Lee
Acute and Critical Care.2022; 37(2): 237. CrossRef
Estructura y función de los equipos de respuesta rápida para la atención de adultos en contextos hospitalarios de alta complejidad: Revisión sistemática de alcance
Juliana Vanessa Rincón-López, Diego Larrotta-Castillo, Kelly Estrada-Orozco, Hernando Gaitán-Duarte
Revista Colombiana de Obstetricia y Ginecología.2021; 72(2): 171. CrossRef
Characteristics and Prognosis of Hospitalized Patients at High Risk of Deterioration Identified by the Rapid Response System: a Multicenter Cohort Study
Sang Hyuk Kim, Ji Young Hong, Youlim Kim
Journal of Korean Medical Science.2021;[Epub] CrossRef
Effects of a Rapid Response Team on the Clinical Outcomes of Cardiopulmonary Resuscitation of Patients Hospitalized in General Wards
Mi-Jung Yoon, Jin-Hee Park
Journal of Korean Academy of Fundamentals of Nursing.2021; 28(4): 491. CrossRef

Rapid response system
Effect of a rapid response system on code rates and in-hospital mortality in medical wards: Hong Yeul Lee, Jinwoo Lee, Sang-Min Lee, Sulhee Kim, Eunjin Yang, Hyun Joo Lee, Hannah Lee, Ho Geol Ryu, Seung-Young Oh, Eun Jin Ha, Sang-Bae Ko, Jaeyoung Cho; Acute Crit Care. 2019;34(4):246-254. Published online November 29, 2019; DOI: https://doi.org/10.4266/acc.2019.00668

5,929 View
193 Download
7 Web of Science
6 Crossref

Abstract PDF

Background
To determine the effects of implementing a rapid response system (RRS) on code rates and in-hospital mortality in medical wards.
Methods
This retrospective study included adult patients admitted to medical wards at Seoul National University Hospital between July 12, 2016 and March 12, 2018; the sample comprised 4,224 patients admitted 10 months before RRS implementation and 4,168 patients admitted 10 months following RRS implementation. Our RRS only worked during the daytime (7 AM to 7 PM) on weekdays. We compared code rates and in-hospital mortality rates between the preintervention and postintervention groups.
Results
There were 62.3 RRS activations per 1,000 admissions. The most common reasons for RRS activation were tachypnea or hypopnea (44%), hypoxia (31%), and tachycardia or bradycardia (21%). Code rates from medical wards during RRS operating times significantly decreased from 3.55 to 0.96 per 1,000 admissions (adjusted odds ratio [aOR], 0.29; 95% confidence interval [CI], 0.10 to 0.87; P=0.028) after RRS implementation. However, code rates from medical wards during RRS nonoperating times did not differ between the preintervention and postintervention groups (2.60 vs. 3.12 per 1,000 admissions; aOR, 1.23; 95% CI, 0.55 to 2.76; P=0.614). In-hospital mortality significantly decreased from 56.3 to 42.7 per 1,000 admissions after RRS implementation (aOR, 0.79; 95% CI, 0.64 to 0.97; P=0.024).
Conclusions
Implementation of an RRS was associated with significant reductions in code rates during RRS operating times and in-hospital mortality in medical wards.

Citations

Citations to this article as recorded by

The role of emergency medical services in the management of in-hospital emergencies: Causes and outcomes of emergency calls – A descriptive retrospective register-based study
Henna Myrskykari, Timo Iirola, Hilla Nordquist
Australasian Emergency Care.2024; 27(1): 42. CrossRef
Effects of a Rapid Response Team on Patient Outcomes: A Systematic Review
Qiuxia Zhang, Khuan Lee, Zawiah Mansor, Iskasymar Ismail, Yi Guo, Qiao Xiao, Poh Ying Lim
Heart & Lung.2024; 63: 51. CrossRef
Society of Critical Care Medicine Guidelines on Recognizing and Responding to Clinical Deterioration Outside the ICU: 2023
Kimia Honarmand, Randy S. Wax, Daleen Penoyer, Geoffery Lighthall, Valerie Danesh, Bram Rochwerg, Michael L. Cheatham, Daniel P. Davis, Michael DeVita, James Downar, Dana Edelson, Alison Fox-Robichaud, Shigeki Fujitani, Raeann M. Fuller, Helen Haskell, Ma
Critical Care Medicine.2024; 52(2): 314. CrossRef
Improving sepsis recognition and management
Merrilee I Cox, Hillary Voss
Current Problems in Pediatric and Adolescent Health Care.2021; 51(4): 101001. CrossRef
A Somogy Megyei Kaposi Mór Oktató Kórház által bevezetett gyors reagálású rendszer hatása a kórházi mortalitásra
János Fogas, Rita Koroseczné Pavlin, Krisztina Szabó, Eszter Héra, Imre Repa, Mariann Moizs
Orvosi Hetilap.2021; 162(20): 782. CrossRef
Evidence revealed the effects of rapid response system
Jae Hwa Cho
Acute and Critical Care.2019; 34(4): 282. CrossRef

Statistical Analysis of Mortality Associated with Anesthesia and Surgery in a Hospital from 2000 to 2004: Jiyeon Sim, Donguk Kim, Jeong Rim Lee, Wonsik Ahn; Korean J Crit Care Med. 2007;22(1):15-24.

1,529 View
14 Download

Abstract PDF: BACKGROUND
Surgical patients should be provided adequate information on operation. The information on mortality is extremely important among them. The purposes of this study are to investigate the recent mortality associated with anesthesia and surgery, and to get a logistic regression model of mortality based on patient information. METHODS: We collected all of the anesthetic cases except local anesthesia during 5 years (between 2000 and 2004) in a hospital. All deaths within 7 days after anesthesia were retrieved. These data were analyzed in terms of age, gender, department in charge, time point after anesthesia, elective or emergency surgery, type of anesthesia, operation name, and diagnosis. The combined effects of the variables on the mortality were evaluated with logistic regression. The causes of death were also analyzed. RESULTS: There were 155 deaths among 74,458 patients under anesthesia. Age less than 1 year old or greater than 80 years old, male gender, department of thoracic surgery, emergency operation, cardiovascular surgery, and diseases for transplantation had higher mortality than their counterparts. Regression model was followed with assignment of '1' for the above mentioned categories. Other categories were designated by '0'. Log[p (death)/{1-p (death)}] = -9.15+1.03xage+0.66xsex+0.79xdepartment+2.77xemergency+2.52 xdiagnosis+0.89xoperation The leading cause of death was sepsis (21.9%). CONCLUSIONS: The average of mortality within 7 days after anesthesia was 21 per 10,000 anesthetic cases (0.21%). Estimated mortality based on logistic regression ranged from 0.01% to 10.25% depending on patient information.

First
Prev
Page of 1
Next
Last

Search