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.
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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.
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Acute Crit Care. 2019;34(4):246-254. Published online November 29, 2019
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.
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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.