non-ICU Beds Research Articles

Telemedicine for emergency patient rescue.

This article summarizes recent developments in the application of telemedicine, specifically tele-critical care (TCC), toward enhancing patient care during various types of emergencies and patient rescue scenarios when there are limited resources in terms of staff expertise (i.e., knowledge, skills, and abilities), staffing numbers, space, and supplies due to patient location (e.g., a non-ICU bed, the emergency department, a rural hospital) or patient volume as in pandemic surges. The COVID-19 pandemic demonstrated the need for rapidly scalable and agile healthcare delivery systems. During the pandemic, clinicians and hospital systems adopted telemedicine for various applications. Taking advantage of technological improvements in cellular networks and personal mobile devices, and despite the limited outcomes literature to support its use, telemedicine was rapidly adopted to address the fundamental challenge of exposure in outpatient settings, emergency departments, patient follow-up, and home-based monitoring. A critical recognition was that the modality of care (e.g., remote vs. in-person) was less important than access to care, regardless of the patient outcomes. This fundamental shift, facilitated by policies that followed emergency declarations, provided an opportunity to maintain and, in many cases, expand and improve clinical practices and hospital systems by bringing expertise to the patient rather than the patient to the expertise. In addition to using telemedicine to maintain patient access to healthcare, TCC was harnessed to provide local clinicians, forced to manage critically ill patients beyond their normal scope of practice or experience, access to remote expertise (physician, nursing, respiratory therapist, pharmacist). These practices supported decades of literature from the telemedicine community describing the effectiveness of telemedicine in improving patient care and the many challenges defining its value. In this review, we summarize numerous examples of innovative care delivery systems that have utilized telemedicine, focusing on 'mobile' TCC technology solutions to effectively deliver the best care to the patient regardless of patient location. We emphasize how a 'paradigm of better' can enhance the entirety of the healthcare system.

Relationship between intensive care surge capacity and hospital factors: an extensive experience.

As a prolonged surge scenario, the COVID-19 pandemic has offered an unparalleled opportunity to improve hospital surge capacity (SC) understanding and the ability to manage it. In this study, the authors report the experience of a large hospital network and evaluate potential relationships between Intensive Care Units SC (ICU-SC) and some hospital-related variables: bed occupancy, emergency department admissions, ward admission from ED, and elective surgery procedures. Pearson's partial correlation coefficient (r) has been used to define the relationship between SC and the daily values of the above variables, collected through a dedicated digital platform that also ensured a regular quality check of the data. The observation has concerned several levels of analysis, namely two different types of SC calculation (SC base-SCb and SC actual-SCa), hospital category level and multi-hospital level, and two consecutive pandemic waves. Among the 16 hospitals observed, the correlation was shown to be moderate-positive with non-ICU bed occupancy (r/ = 0.62, r/ = 0.54), strong/moderate with ICU bed occupancy (r/ = 0.72, r/ = 0.54), and moderate with ward admissions from ED (r/ = 0.50, r/ = 0.51) On the contrary, the correlation proved to be moderate-negative with ED admissions (r/ = - 0.69, r/ = - 0.62) and low with the number of elective surgery procedures (r/ = - 0.10, r/ = - 0.16). This study identified a positive correlation between SC and three variables monitored: ICU bed occupancy, non-ICU bed occupancy, and ward admissions from ED. On the contrary, the correlation was negative for ED admission and the number of elective surgery procedures. The results have been confirmed across all levels of analysis adopted.

Prediction of hospitalisations based on wastewater-based SARS-CoV-2 epidemiology

Wastewater-based epidemiology is widely applied in Austria since April 2020 to monitor the SARS-CoV-2 pandemic. With a steadily increasing number of monitored wastewater facilities, 123 plants covering roughly 70 % of the 9 million population were monitored as of August 2022. In this study, the SARS-CoV-2 viral concentrations in raw sewage were analysed to infer short-term hospitalisation occupancy. The temporal lead of wastewater-based epidemiological time series over hospitalisation occupancy levels facilitates the construction of forecast models. Data pre-processing techniques are presented, including the approach of comparing multiple decentralised wastewater signals with aggregated and centralised clinical data. Time‑lead quantification was performed using cross-correlation analysis and coefficient of determination optimisation approaches. Multivariate regression models were successfully applied to infer hospitalisation bed occupancy. The results show a predictive potential of viral loads in sewage towards Covid-19 hospitalisation occupancy, with an average lead time towards ICU and non-ICU bed occupancy between 14.8-17.7 days and 8.6–11.6 days, respectively. The presented procedure provides access to the trend and tipping point behaviour of pandemic dynamics and allows the prediction of short-term demand for public health services. The results showed an increase in forecast accuracy with an increase in the number of monitored wastewater treatment plants. Trained models are sensitive to changing variant types and require recalibration of model parameters, likely caused by immunity by vaccination and/or infection. The utilised approach displays a practical and rapidly implementable application of wastewater-based epidemiology to infer hospitalisation occupancy.

Balanced Crystalloids Versus Normal Saline in Noncritically Ill Adults with Sickle Cell Disease Admitted from the Emergency Department: A Sub-Analysis of the Salt-ED Trial

Background: Common acute manifestations of sickle cell disease (SCD), including vaso-occlusive pain episodes (VOE) and other complications, are often treated in the emergency department (ED). Effective treatment in the ED remains challenging and often only includes supportive care, such as analgesics and intravenous (IV) fluids. There is clinical equipoise regarding the type of IV fluid to use for resuscitation, and while normal saline (NS) is the most commonly one used in the ED, some SCD providers discourage use of NS. Patients with SCD and kidney dysfunction, who are often at-risk for acidosis already, may not effectively excrete the high sodium, hyperosmolar load of NS, and in large volumes NS may cause hyperchloremic metabolic acidosis that could impact sickle red blood cell (sRBC) rheology (Nath, Nat Rev Nephrol, 2015). We recently demonstrated modestly hyperosmolar and acidotic fluids like NS (308mOsm/L, pH=5.5) may dehydrate sRBCs, increase sRBC/endothelium adhesion, and worsen deformability; and we also showed NS bolus use in pediatric pain management in the ED was associated with inferior pain control, increased likelihood of hospital admission, and more time spent in the ED (Carden et al., Blood & AJH, 2017&2019). Rigorous studies examining the impact of IV fluids, particularly NS, during ED management of adults with SCD have not been performed, and recent evidence suggests that balanced crystalloids (B) may be safer for resuscitation of adults in the ED. Methods: We utilized data from the seminal SALT-ED study, a single-center, pragmatic, multiple-crossover prospectively randomized controlled trial (RCT) that compared outcomes related to administration of balanced crystalloids (primarily lactated ringers [LR], 274mOsm/L, pH=6.5) to NS administration to all-comer adult patients in the ED (Self et al., NEJM, 2018). Using ICD-10 codes, we conducted an intention-to-treat sub-analysis of SCD patients in the SALT-ED trial. Enrolled participants were non-critically ill adults (18 years and older) with SCD who presented to the ED and were subsequently hospitalized to a non-ICU bed at the hospital. Chief complaint was captured when available. The primary outcome examined was median hospital free days post-index hospitalization and secondary outcomes included a composite of adverse kidney events and death (i.e. MAKE30), as well as changes in serum electrolyte status. Results: There were 150 adults with SCD identified in the SALT-ED study (Table 1a). Of 13,347 visits captured, 247 ED visits were attributed to these 150 patients - N=126 visits (51%) in the B group and N=121 (49%) in the NS group. Over 50% of documented chief complaints in the ED were pain-related. Compared to all adult patients in the SALT-ED study, those with SCD were younger, more likely to identify as female and Black, have a lower baseline creatinine, and while having similar hospital free days and similar volumes of crystalloids administered in the ED, patients with SCD had an approximate 3.5 times higher odds of suffering an acute kidney injury event (i.e. MAKE30 endpoint) during hospitalization. Compared to those with SCD randomized to the B group, those patients with SCD in the NS group had more adverse kidney events, increased mean length of hospital stay, higher maximum in-hospital sodium, higher maximum in-hospital chloride, and lower minimum in-hospital bicarbonate, suggesting an increased risk of high sodium acidosis with NS use in the ED (Table 1a&b). Using combined data from both groups and analyzing with linear regression, we found in-hospital electrolyte levels were significantly associated with hospital length of stay among adults with SCD administered IV fluids in the ED (Table 1c). Conclusion: The results from this large RCT sub-analysis further support recent evidence suggesting NS is not an ideal resuscitation IV fluid for adults with SCD presenting to the ED with pain and other complications. Adult patients with SCD randomized to NS in the ED had higher rates of in-hospital high sodium levels, metabolic acidosis, increased hospital stay, and increased kidney events. Our findings suggest that administering balanced crystalloids like LR may be a better option for IV fluid resuscitation among adults with SCD presenting to the ED. Further research is needed to confirm these findings and to determine the optimal IV fluid regimen for adults with SCD presenting to the ED with VOE or other complications. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

What are the main factors affecting the outcome of tocilizumab therapy in COVID-19-induced cytokine release syndrome?

Recommendations for the treatment of cytokine release syndrome/macrophage activation syndrome (MAS) associated with coronavirus disease-2019 (COVID-19) are still of poor quality. IL-6 is an important therapeutic target as a main mediator of cytokine storm. The aim of our study was to evaluate the tocilizumab (TCZ) efficacy and factors affecting the therapy outcome. This retrospective study included 27 patients treated with TCZ for COVID-19-MAS. All patients in this study were treated with TCZ (intravenously, at a dose of 8 mg kg1 ) in addition to standard therapy. Clinical improvement (survival and decreased oxygen demand) on the 10-14th days and secondary infection rate were assessed. In our 27 treated patients, 14 (51.8%) received TCZ in the intensive care unit (ICU) and seven (25.9%) were need to invasive mechanical ventilation (IMV). Fifteen (55.6%) of these patients revealed a good clinical response (four patients discharge from the ICU and 11 patients who followed-up in nonICU beds showed a decrease in oxygen demand). TCZ was significantly less effective in patients having high Murray lung injury score, low PO2/FiO2 ratio, IMV, and ICU admission (P < .05). Severity of hypoxemia was found as a single independent risk factor in the multivariable analysis (P < .05). Secondary bacterial infections rate was significantly higher in intubated patients (P < .01) or treated in the ICU (P ¼ .01). TCZ was showed limited efficacy for COVID-19-related MAS. The most important predictive indicator for therapy outcome was found as the severity of hypoxemia. In addition, IMV and/or ICU was associated with the poor outcome and high side effect. So, controlled trials are still needed to confirm the indications and timing of TCZ therapy.

A retrospective cohort study of short-stay admissions to the medical intensive care unit: Defining patient characteristics and critical care resource utilization.

Little is known about the mortality and utilization outcomes of short-stay intensive care unit (ICU) patients who require <24 h of critical care. We aimed to define characteristics and outcomes of short-stay ICU patients whose need for ICU level-of-care is ≤24 h compared to nonshort-stay patients. Single-center retrospective cohort study of patients admitted to the medical ICU at an academic tertiary care center in 2019. Fisher's exact test or Chi-square for descriptive categorical variables, t-test for continuous variables, and Mann-Whitney two-sample test for length of stay (LOS) outcomes. Of 819 patients, 206 (25.2%) were short-stay compared to 613 (74.8%) nonshort-stay. The severity of illness as measured by the Mortality Probability Model-III was significantly lower among short-stay compared to nonshort-stay patients (P = 0.0001). Most short-stay patients were admitted for hemodynamic monitoring not requiring vasoactive medications (77, 37.4%). Thirty-six (17.5%) of the short-stay cohort met Society of Critical Care Medicine's guidelines for ICU admission. Nonfull-ICU LOS, or time spent waiting for transfer out to a non-ICU bed, was similar between the two groups. Hospital mortality was lower among short-stay patients compared to nonshort-stay patients (P = 0.01). Despite their lower illness severity and fewer ICU-level care needs, short-stay patients spend an equally substantial amount of time occupying an ICU bed while waiting for a floor bed as nonshort-stay patients. Further investigation into the factors influencing ICU triage of these subacute patients and contributors to system inefficiencies prohibiting their timely transfer may improve ICU resource allocation, hospital throughput, and patient outcomes.

Incorporation of near-real-time hospital occupancy data to improve hospitalization forecast accuracy during the COVID-19 pandemic.

Public health decision makers rely on hospitalization forecasts to inform COVID-19 pandemic planning and resource allocation. Hospitalization forecasts are most relevant when they are accurate, made available quickly, and updated frequently. We rapidly adapted an agent-based model (ABM) to provide weekly 30-day hospitalization forecasts (i.e., demand for intensive care unit [ICU] beds and non-ICU beds) by state and region in North Carolina for public health decision makers. The ABM was based on a synthetic population of North Carolina residents and included movement of agents (i.e., patients) among North Carolina hospitals, nursing homes, and the community. We assigned SARS-CoV-2 infection to agents using county-level compartmental models and determined agents’ COVID-19 severity and probability of hospitalization using synthetic population characteristics (e.g., age, comorbidities). We generated weekly 30-day hospitalization forecasts during May–December 2020 and evaluated the impact of major model updates on statewide forecast accuracy under a SARS-CoV-2 effective reproduction number range of 1.0–1.2. Of the 21 forecasts included in the assessment, the average mean absolute percentage error (MAPE) was 7.8% for non-ICU beds and 23.6% for ICU beds. Among the major model updates, integration of near-real-time hospital occupancy data into the model had the largest impact on improving forecast accuracy, reducing the average MAPE for non-ICU beds from 6.6% to 3.9% and for ICU beds from 33.4% to 6.5%. Our results suggest that future pandemic hospitalization forecasting efforts should prioritize early inclusion of hospital occupancy data to maximize accuracy.

Perceptions Regarding the SARS-CoV-2 Pandemic's Impact on Neurocritical Care Delivery: Results From a Global Survey.

Background:The SARS-CoV-2 (COVID-19) pandemic has impacted many facets of critical care delivery.Methods:An electronic survey was distributed to explore the pandemic’s perceived impact on neurocritical care delivery between June 2020 and March 2021. Variables were stratified by World Bank country income level, presence of a dedicated neurocritical care unit (NCCU) and experiencing a COVID-19 patient surge.Results:Respondents from 253 hospitals (78.3% response rate) from 47 countries (45.5% low/middle income countries; 54.5% with a dedicated NCCU; 78.6% experienced a first surge) participated in the study. Independent of country income level, NCCU and surge status, participants reported reductions in NCCU admissions (67%), critical care drug shortages (69%), reduction in ancillary services (43%) and routine diagnostic testing (61%), and temporary cancellation of didactic teaching (44%) and clinical/basic science research (70%). Respondents from low/middle income countries were more likely to report lack of surge preparedness (odds ratio [OR], 3.2; 95% confidence interval [CI], 1.8-5.8) and struggling to return to prepandemic standards of care (OR, 12.2; 95% CI, 4.4-34) compared with respondents from high-income countries. Respondents experiencing a surge were more likely to report conversion of NCCUs and general-mixed intensive care units (ICUs) to a COVID-ICU (OR 3.7; 95% CI, 1.9-7.3), conversion of non-ICU beds to ICU beds (OR, 3.4; 95% CI, 1.8-6.5), and deviations in critical care and pharmaceutical practices (OR, 4.2; 95% CI 2.1-8.2). Respondents from hospitals with a dedicated NCCU were less likely to report conversion to a COVID-ICU (OR, 0.5; 95% CI, 0.3-0.9) or conversion of non-ICU to ICU beds (OR, 0.5; 95% CI, 0.3-0.9).Conclusion:This study reports the perceived impact of the COVID-19 pandemic on global neurocritical care delivery, and highlights shortcomings of health care infrastructures and the importance of pandemic preparedness.

Medical gas pipeline system as a limiting factor for hospital oxygen supply during COVID-19 outbreak.

Medical gas pipeline system as a limiting factor for hospital oxygen supply during COVID-19 outbreak.

Paediatric cardiac rapid response systems: a survey of multicentre practices.

While the efficacy and guidelines for implementation of rapid response systems are well established, limited information exists about rapid response paradigms for paediatric cardiac patients despite their unique pathophysiology. With endorsement from the Paediatric Cardiac Intensive Care Society, we designed and implemented a web-based survey of paediatric cardiac and multidisciplinary ICU medical directors in the United States of America and Canada to better understand paediatric cardiac rapid response practices. Sixty-five (52%) of 125 centres responded. Seventy-one per cent of centres had ∼300 non-ICU beds and 71% had dedicated cardiac ICUs. To respond to cardiac patients, dedicated cardiac rapid response teams were utilised in 29% of all centres (39% and 5% in centres with and without dedicated cardiac ICUs, respectively) [p = 0.006]. Early warning scores were utilised in 62% of centres. Only 31% reported that rapid response teams received specialised training. Transfers to ICU were higher for cardiac (73%) compared to generalised rapid response events (54%). The monitoring and reassessment of patients not transferred to ICU after the rapid response was variable. Cardiac and respiratory arrests outside the ICU were infrequent. Only 29% of centres formally appraise critical deterioration events (need for ventilation and/or inotropes post-rapid response) and 34% perform post-event debriefs. Paediatric cardiac rapid response practices are variable and dedicated paediatric cardiac rapid response systems are infrequent in the United States of America and Canada. Opportunity exists to delineate best practices for paediatric cardiac rapid response and standardise practices for activation, training, patient monitoring post-rapid response events, and outcomes evaluation.

Model Input and Optimization: Improving the Speed and Accuracy of Our COVID-19 Hospitalization Forecasts

Background: During the COVID-19 pandemic, public-health decision makers have increasingly relied on hospitalization forecasts that are routinely provided, accurate, and based on timely input data to inform pandemic planning. In North Carolina, we adapted an existing agent-based model (ABM) to produce 30-day hospitalization forecasts of COVID-19 and non–COVID-19 hospitalizations for use by public-health decision makers. We sought to continually improve model speed and accuracy during forecasting. Methods: The geospatially explicit ABM included movement of agents (ie, patients) among 104 short-term acute-care hospitals, 10 long-term acute-care hospitals, 421 licensed nursing homes, and the community in North Carolina. Agents were based on a synthetic population of North Carolina residents (ie, &gt;10.4 million agents). We assigned SARS-CoV-2 infections to agents according to county-level susceptible, exposed, infectious, recovered (SEIR) models informed by reported COVID-19 cases by county. Agents’ COVID-19 severity and probability of hospitalization were determined using agent-specific characteristics (eg, age, comorbidities). During May 2020–December 2020, we produced weekly 30-day forecasts of intensive care unit (ICU) and non-ICU bed occupancy for COVID-19 agents and non–COVID-19 agents statewide and by region under a range of SARS-CoV-2 effective reproduction numbers. During the reporting period, we identified optimizations for faster results turnaround. We evaluated the incorporation of real-time hospital-level occupancy data at model initialization on forecast accuracy using mean absolute percent error (MAPE). Results: During May 2020–December 2020, we provided 31 weekly reports of 30-day hospitalization forecasts with a 1-day turnaround time. Reports included (1) raw and smoothed 7-day average values for 42 model output variables; (2) static visuals of ICU and non-ICU bed demand and capacity; and (3) an interactive Tableau workbook of hospital demand variables. Identifying code efficiencies reduced a single model runtime from ~100 seconds to 28 seconds. The use of cloud computing reduced simulation runtime from ~20 hours to 15 minutes. Across forecasts, the average MAPEs were 21.6% and 7.1% for ICU and non-ICU bed demand, respectively. By incorporating hospital-level occupancy data, we reduced the average MAPE to 6.5% for ICU bed demand and 3.9% for non-ICU bed demand, indicating improved accuracy. Conclusions: We adapted an ABM and continually improved it during COVID-19 forecasting by optimizing code and computing resources and including real-time hospital-level occupancy data. Planned SEIR model updates for enhanced forecasts include the addition of compartments for undocumented infections and recoveries as well as permission of reinfection from recovered compartments.Funding: NoDisclosures: None

Flexibility and Bed Margins of the Community of Madrid's Hospitals during the First Wave of the SARS-CoV-2 Pandemic.

Background: The COVID-19 pandemic has had global effects; cases have been counted in the tens of millions, and there have been over two million deaths throughout the world. Health systems have been stressed in trying to provide a response to the increasing demand for hospital beds during the different waves. This paper analyzes the dynamic response of the hospitals of the Community of Madrid (CoM) during the first wave of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in the period between 18 March and 31 May 2020. The aim was to model the response of the CoM’s health system in terms of the number of available beds. Methods: A research design based on a case study of the CoM was developed. To model this response, we use two concepts: “bed margin” (available beds minus occupied beds, expressed as a percentage) and “flexibility” (which describes the ability to adapt to the growing demand for beds). The Linear Hinges Model allowed a robust estimation of the key performance indicators for capturing the flexibility of the available beds in hospitals. Three new flexibility indicators were defined: the Average Ramp Rate Until the Peak (ARRUP), the Ramp Duration Until the Peak (RDUP), and the Ramp Growth Until the Peak (RGUP). Results: The public and private hospitals of the CoM were able to increase the number of available beds from 18,692 on 18 March 2020 to 23,623 on 2 April 2020. At the peak of the wave, the number of available beds increased by 160 in 48 h, with an occupancy of 90.3%. Within that fifteen-day period, the number of COVID-19 inpatients increased by 200% in non-intensive care unit (non-ICU) wards and by 155% in intensive care unit (ICU) wards. The estimated ARRUP for non-ICU beds in the CoM hospital network during the first pandemic wave was 305.56 beds/day, the RDUP was 15 days, and the RGUP was 4598 beds. For the ICU beds, the ARRUP was 36.73 beds/day, the RDUP was 20 days, and the RGUP was 735 beds. This paper includes a further analysis of the response estimated for each hospital. Conclusions: This research provides insights not only for academia, but also for hospital management and practitioners. The results show that not all of the hospitals dealt with the sudden increase in bed demand in the same way, nor did they provide the same flexibility in order to increase their bed capabilities. The bed margin and the proposed indicators of flexibility summarize the dynamic response and can be included as part of a hospital’s management dashboard for monitoring its behavior during pandemic waves or other health crises as a complement to other, more steady-state indicators.

Adherence to care transitions recommendations among high-risk hospitalized older patients.

Evidence on the effectiveness of inpatient hospital geriatric consultation is scant, and it is unknown whether adherence to specific recommendations will improve care and patient outcomes. This study was conducted to provide insights from a quality improvement project that may help guide further improvements in the effectiveness of these consultations made as a component of a care transitions program (CTP). Secondary analysis of the implementation of a multicomponent CTP for high-risk hospitalized patients aged 75 and older. A 400-bed community teaching hospital. Two hundred and two patients admitted to non-ICU beds who met high-risk criteria. Inpatient comprehensive geriatric consultation including care transition recommendations, telephone and in-person follow-up weekly for 4 weeks after discharge, and collaboration with post-acute organizations and primary care and specialist physicians to implement recommendations. Primary outcomes for this analysis was 30-day hospital readmissions and adherence to transition of care recommendations. The 142 patients with at least one post-discharge visit received 936 care transition recommendations. Overall, 663 (71%) of the 936 care transition recommendations were adhered to (71%). The adherence rate was lower in the 22 patients who were readmitted to the hospital within 30 days (63%) compared to 72% adherence in the 120 patients who were not readmitted. This was not a statistically significant difference, and there were no significant differences in the number and percent adherence in any recommendation category between the two groups. We found adherence to just over two-thirds of care transition recommendations, similar to a small number of other studies. We did not find a relationship between the number of recommendations and adherence to them with 30-day readmissions to the hospital. Future studies of CTPs should consider several strategies may enhance geriatric consultation care transitions recommendations and adherence to them, and improve patient outcomes.

Delay in ICU transfer is protective against ICU readmission in trauma patients: a naturally controlled experiment

BackgroundUnplanned intensive care unit (ICU) readmission—ICU bounce back (ICUbb)—is associated with worse outcomes. Patients not requiring organ system support or intensive nursing are deemed ‘ICU discharge ready’ and transfer orders...

Nowcasting and Forecasting the Spread of COVID-19 and Healthcare Demand in Turkey, a Modeling Study.

Background: This study aims to estimate the total number of infected people, evaluate the effects of NPIs on the healthcare system, and predict the expected number of cases, deaths, hospitalizations due to COVID-19 in Turkey.Methods: This study was carried out according to three dimensions. In the first, the actual number of infected people was estimated. In the second, the expected total numbers of infected people, deaths, hospitalizations have been predicted in the case of no intervention. In the third, the distribution of the expected number of infected people and deaths, and ICU and non-ICU bed needs over time has been predicted via a SEIR-based simulator (TURKSAS) in four scenarios.Results: According to the number of deaths, the estimated number of infected people in Turkey on March 21 was 123,030. In the case of no intervention the expected number of infected people is 72,091,595 and deaths is 445,956, the attack rate is 88.1%, and the mortality ratio is 0.54%. The ICU bed capacity in Turkey is expected to be exceeded by 4.4-fold and non-ICU bed capacity by 3.21-fold. In the second and third scenarios compliance with NPIs makes a difference of 94,303 expected deaths. In both scenarios, the predicted peak value of occupied ICU and non-ICU beds remains below Turkey's capacity.Discussion: Predictions show that around 16 million people can be prevented from being infected and 94,000 deaths can be prevented by full compliance with the measures taken. Modeling epidemics and establishing decision support systems is an important requirement.

Reduction of Intensive Care Unit Length of Stay: The Case of Early Mobilization.

Bed rest or immobilization is frequently part of treatment for patients in the intensive care unit (ICU) with critical illness. The average ICU length of stay (LOS) is 3.3 days, and for every day spent in an ICU bed, the average patient spends an additional 1.5 days in a non-ICU bed. The purpose of this research study was to analyze the effects of early mobilization for patients in the ICU to determine if it has an impact on the LOS, cost of care, and medical complications. The methodology for this study was a literature review. Five electronic databases were used, with a total of 26 articles referenced for this research. Early mobilization suggested a decrease in delirium by 2 days, reduced risk of readmission or death, and reduced ventilator-assisted pneumonia, central line, and catheter infections. Length of stay in the ICU was reduced with statistical significance in several studies examining early mobilization. Limited research on cost of ICU LOS indicated potential savings with early mobilization. When implementing early mobilization in the ICU, total costs were decreased and medical complications were reduced. Early mobilization should become a standard of care for critically ill but stable patients in the ICU.

Adapting STEMI care for the COVID-19 pandemic: The case for low-risk STEMI triage and early discharge.

The coronavirus pandemic has resulted in the need for rapid assessment of resource utilization within our hospital systems. Specifically, the overwhelming need for intensive care unit (ICU) beds within epicenters of the pandemic has created a need for consideration as to how acute coronary syndrome cases, and specifically ST‐elevation myocardial infarction (STEMI) patients, are managed postprocedure. While most patients in the United States continue to be managed in coronary care units after primary percutaneous coronary intervention, there is a robust literature regarding the ability to triage STEMI patients safely and efficiently with low‐risk features to non‐ICU beds. We review the various risk scores for STEMI triage and the data supporting their usage. In summary, these findings support an approach to low‐risk STEMI triage that does not come at the expense of quality patient care or outcomes, where up to two‐thirds of patients with STEMI may be able to be safely managed without ICU‐level care.

Predicted Effects of Stopping COVID-19 Lockdown on Italian Hospital Demand.

Italy has been one of the first countries to implement mitigation measures to curb the coronavirus disease 2019 (COVID-19) pandemic. There is currently a debate on when and how such measures should be loosened. To forecast the demand for hospital intensive care unit (ICU) and non-ICU beds for COVID-19 patients from May to September, we developed 2 models, assuming a gradual easing of restrictions or an intermittent lockdown. We used a compartmental model to evaluate 2 scenarios: (A) an intermittent lockdown; (B) a gradual relaxation of the lockdown. Predicted ICU and non-ICU demand was compared with the peak in hospital bed use observed in April 2020. Under scenario A, while ICU demand will remain below the peak, the number of non-ICU will substantially rise and will exceed it (133%; 95% confidence interval [CI]: 94-171). Under scenario B, a rise in ICU and non-ICU demand will start in July and will progressively increase over the summer 2020, reaching 95% (95% CI: 71-121) and 237% (95% CI: 191-282) of the April peak. Italian hospital demand is likely to remain high in the next months. If restrictions are reduced, planning for the next several months should consider an increase in health-care resources to maintain surge capacity across the country.

The Association between Glucose Levels and Hospital Outcomes in Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease

Introduction: Chronic obstructive pulmonary disease (COPD) is the fourth most common cause of death in the world and will become the third most common in a few years. Identification of prognostic factors may lead to improved treatment strategies and clinical outcomes for COPD. To find out the association between glucose levels and hospital outcomes in patients with acute exacerbations of chronic obstructive pulmonary disease.&#x0D; Materials and Methods: This retrospective study was carried out different privet and public hospital in Chandpur. The information reported in this manuscript was collected during three separate projects in the same hospital. The first cohort included 35 COPD patients admitted for an acute exacerbation to a non-ICU bed to determine if there was a relationship between corticosteroid dosage and hyperglycemia. The second cohort included 210 COPD patients with acute exacerbations who were admitted to the MICU to determine factors associated with mortality and LOS. The third cohort included 105 COPD patients who were monitored for re-admission within 30 days after discharge for an acute exacerbation.&#x0D; Results: Mean arterial pressure, hemoglobin, blood urea nitrogen, albumin, nursing home, pleural effusion and intubation were statistically significant (p&lt;0.05) between two group. Intubation had 5.318 (95% CI 2.578 to 11.721) times increase in odds having length of stay. Intubation was significantly associated with length of stay.&#x0D; Conclusion: Mean arterial pressure, hemoglobin, blood urea nitrogen, albumin, nursing home, pleural effusion and intubation were statistically significant between in hospital deaths group and survivors group.&#x0D; Medicine Today 2020 Vol.32(1): 62-66

Diabetic Ketoacidosis Management in the Emergency Department: Implementation of a Protocol to Reduce Variability and Improve Safety.

Emergency departments (EDs) are the primary venue for diagnosis and initiation of treatment of diabetic ketoacidosis (DKA). Typically managed in an intensive care unit (ICU), in many medical centers, limited ICU bed availability necessitates DKA-extended ED management until sufficient improvement for admission to a non-ICU bed. Although DKA treatment is well established, coordinating safe and effective extended care in a busy ED is complex. Recurrent problems in this high-risk transition of care led us to improvement efforts. We studied the impact of a standardizing ED DKA management in two phases: rollout of a DKA pathway in our computerized order entry system followed by audit and feedback. We evaluated adherence, clinical process, operational, and safety measures following these interventions. Adherence to the pathway was initially slow, improving significantly after audit and feedback. We observed mixed improvements in clinical processes, no changes in operational metrics (as expected), and reductions in variability for several measures. There were no deteriorations and improvements in measures of safety, and a reduction in the number of adverse event reports in the postimplementation periods.