scholarly journals A Model of Bed Demand to Facilitate the Implementation of Data-driven Recommendations for COVID-19 Capacity Management

2020 ◽  
Author(s):  
Teng Zhang ◽  
Kelly McFarlane ◽  
Jacqueline Vallon ◽  
Linying Yang ◽  
Jin Xie ◽  
...  
Keyword(s):  
Intensive Care ◽  
Acute Care ◽  
Capacity Planning ◽  
Medical Center ◽  
Capacity Management ◽  
Data Driven ◽  
Severe Illness ◽  
Hospital Capacity ◽  
Medicine Service ◽  
Number Of Patients

Abstract Background:We sought to build an accessible interactive model that could facilitate hospital capacity planning in the presence of significant uncertainty about the proportion of the population that is positive forcoronavirus disease 2019 (COVID-19) and the rate at which COVID-19 is spreading in the population. Our goal was to facilitate the implementation of data-driven recommendations for capacity management with a transparent mathematical simulation designed to answer the specific, local questions hospital leadership considered critical.Methods:The model facilitates hospital planning with estimates of the number of Intensive Care (IC) beds, Acute Care (AC) beds, and ventilators necessary to accommodate patients who require hospitalization for COVID-19 and how these compare to the available resources. Inputs to the model include estimates of the characteristics of the patient population and hospital capacity. We deployed this model as an interactive online tool with modifiable parameters.Results:The use of the model is illustrated by estimating the demand generated by COVID-19+ arrivals for a hypothetical acute care medical center. The model calculated that the number of patients requiring an IC bed would equal the number of IC beds on Day 23, the number of patients requiring a ventilator would equal the number of ventilators available on Day 27, and the number of patients requiring an AC bed and coverage by the Medicine Service would equal the capacity of the Medicine service on Day 21. The model was used to inform COVID-19 planning and decision-making, including Intensive Care Unit (ICU) staffing and ventilator procurement.Conclusion:In response to the COVID-19 epidemic, hospitals must understand their current and future capacity to care for patients with severe illness. While there is significant uncertainty around the parameters used to develop this model, the analysis is based on transparent logic and starts from observed data to provide a robust basis of projections for hospital managers. The model demonstrates the need and provides an approach to address critical questions about staffing patterns for IC and AC, and equipment capacity such as ventilators.Contributions to the literature:· Generation and implementation of data-driven recommendations for hospital capacity management early in the COVID-19 pandemic· The conceptualization, development, and deployment of an interactive simulation model in two weeks· Data-driven capacity management in the presence of significant uncertainty about the expected volume of patients, their clinical needs, and the availability of the workforceTrial Registration: Not applicable

scholarly journals A model to estimate bed demand for COVID-19 related hospitalization

2020 ◽  
Author(s):  
Teng Zhang ◽  
Kelly McFarlane ◽  
Jacqueline Vallon ◽  
Linying Yang ◽  
Jin Xie ◽  
...  
Keyword(s):  
Intensive Care ◽  
Acute Care ◽  
Capacity Planning ◽  
The United States ◽  
World Health ◽  
Sensitivity Analyses ◽  
Severe Illness ◽  
Hospital Capacity ◽  
Medicine Service ◽  
Number Of Patients

AbstractAs of March 23, 2020 there have been over 354,000 confirmed cases of coronavirus disease 2019 (COVID-19) in over 180 countries, the World Health Organization characterized COVID-19 as a pandemic, and the United States (US) announced a national state of emergency.1, 2, 3 In parts of China and Italy the demand for intensive care (IC) beds was higher than the number of available beds.4, 5 We sought to build an accessible interactive model that could facilitate hospital capacity planning in the presence of significant uncertainty about the proportion of the population that is COVID-19+ and the rate at which COVID-19 is spreading in the population. Our approach was to design a tool with parameters that hospital leaders could adjust to reflect their local data and easily modify to conduct sensitivity analyses.We developed a model to facilitate hospital planning with estimates of the number of Intensive Care (IC) beds, Acute Care (AC) beds, and ventilators necessary to accommodate patients who require hospitalization for COVID-19 and how these compare to the available resources. Inputs to the model include estimates of the characteristics of the patient population and hospital capacity. We deployed this model as an interactive online tool.6 The model is implemented in R 3.5, RStudio, RShiny 1.4.0 and Python 3.7. The parameters used may be modified as data become available, for use at other institutions, and to generate sensitivity analyses.We illustrate the use of the model by estimating the demand generated by COVID-19+ arrivals for a hypothetical acute care medical center. The model calculated that the number of patients requiring an IC bed would equal the number of IC beds on Day 23, the number of patients requiring a ventilator would equal the number of ventilators available on Day 27, and the number of patients requiring an AC bed and coverage by the Medicine Service would equal the capacity of the Medicine service on Day 21.In response to the COVID-19 epidemic, hospitals must understand their current and future capacity to care for patients with severe illness. While there is significant uncertainty around the parameters used to develop this model, the analysis is based on transparent logic and starts from observed data to provide a robust basis of projections for hospital managers. The model demonstrates the need and provides an approach to address critical questions about staffing patterns for IC and AC, and equipment capacity such as ventilators.

scholarly journals End-of-Life Decision-Making in Intensive Care Ten Years after a Law on Advance Directives in Germany

Medicina ◽  
2021 ◽  
Vol 57 (9) ◽  
pp. 930
Author(s):  
Jan A. Graw ◽  
Fanny Marsch ◽  
Claudia D. Spies ◽  
Roland C. E. Francis
Keyword(s):  
Decision Making ◽  
Intensive Care ◽  
End Of Life ◽  
Advance Directives ◽  
Advance Directive ◽  
Medical Center ◽  
Relevant Information ◽  
Number Of Patients ◽  
Before And After ◽  
Ethical Approval

Background and Objectives: Mortality on Intensive Care Units (ICUs) is high and death frequently occurs after decisions to limit life-sustaining therapies. An advance directive is a tool meant to preserve patient autonomy by guiding anticipated future treatment decisions once decision-making capacity is lost. Since September 2009, advance directives are legally binding for the caregiver team and the patients’ surrogate decision-maker in Germany. The change in frequencies of end-of-life decisions (EOLDs) and completed advance directives among deceased ICU patients ten years after the enactment of a law on advance directives in Germany is unknown. Materials and Methods: Retrospective analysis on all deceased patients of surgical ICUs of a German university medical center from 08/2008 to 09/2009 and from 01/2019 to 09/2019. Frequency of EOLDs and advance directives and the process of EOLDs were compared between patients admitted before and after the change in legislation. (No. of ethical approval EA2/308/20) Results: Significantly more EOLDs occurred in the 2019 cohort compared to the 2009 cohort (85.8% vs. 70.7% of deceased patients, p = 0.006). The number of patients possessing an advance directive to express a living or therapeutic will was higher in the 2019 cohort compared to the 2009 cohort (26.4% vs. 8.9%; difference: 17.5%, p < 0.001). Participation of the patients’ family in the EOLD process (74.7% vs. 60.9%; difference: 13.8%, p = 0.048) and the frequency of documentation of EOLD-relevant information (50.0% vs. 18.7%; difference: 31.3%, p < 0.001) increased from 2009 to 2019. Discussion: During a ten-year period from 2009 to 2019, the frequency of EOLDs and the completion rate of advance directives have increased considerably. In addition, EOLD-associated communication and documentation have further improved.

scholarly journals Modelling intensive care unit capacity under different epidemiological scenarios of the COVID-19 pandemic in three Western European countries

2021 ◽  
Author(s):  
Ruth McCabe ◽  
Mara D Kont ◽  
Nora Schmit ◽  
Charles Whittaker ◽  
Alessandra Løchen ◽  
...  
Keyword(s):  
Intensive Care ◽  
Capacity Planning ◽  
Winter Season ◽  
Winter Period ◽  
Spare Capacity ◽  
Hospital Capacity ◽  
Unit Capacity ◽  
Patient Model ◽  
Hospital Capacity Planning ◽  
Bed Numbers

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic has placed enormous strain on intensive care units (ICUs) in Europe. Ensuring access to care, irrespective of COVID-19 status, in winter 2020–2021 is essential. Methods An integrated model of hospital capacity planning and epidemiological projections of COVID-19 patients is used to estimate the demand for and resultant spare capacity of ICU beds, staff and ventilators under different epidemic scenarios in France, Germany and Italy across the 2020–2021 winter period. The effect of implementing lockdowns triggered by different numbers of COVID-19 patients in ICUs under varying levels of effectiveness is examined, using a ‘dual-demand’ (COVID-19 and non-COVID-19) patient model. Results Without sufficient mitigation, we estimate that COVID-19 ICU patient numbers will exceed those seen in the first peak, resulting in substantial capacity deficits, with beds being consistently found to be the most constrained resource. Reactive lockdowns could lead to large improvements in ICU capacity during the winter season, with pressure being most effectively alleviated when lockdown is triggered early and sustained under a higher level of suppression. The success of such interventions also depends on baseline bed numbers and average non-COVID-19 patient occupancy. Conclusion Reductions in capacity deficits under different scenarios must be weighed against the feasibility and drawbacks of further lockdowns. Careful, continuous decision-making by national policymakers will be required across the winter period 2020–2021.

scholarly journals Diabetes Medication Reconciliation at Hospital Discharge

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A424-A424
Author(s):  
Nandi Shah ◽  
Kristen Kulasa
Keyword(s):  
Hospital Discharge ◽  
Educational Intervention ◽  
Medication Reconciliation ◽  
Medical Center ◽  
Hospital Medicine ◽  
Inpatient Diabetes ◽  
Discharge Medication ◽  
Medicine Service ◽  
Number Of Patients ◽  
Primary Service

Abstract Background: During hospital discharge, patients are at high risk for medication discrepancies as they transition from hospital to home. This study aims to evaluate the prevalence of medication errors at hospital discharge for diabetes medications in patients who received an endocrinology consultation for diabetes and explore interventions to improve the accuracy of discharge medication reconciliation. Methods: All patients (n=3018) who received an endocrinology consultation for diabetes at a tertiary care medical center from October 2017 to December 2019 were included. A retrospective chart review was performed to collect the following information on each patient: primary service from which the patient was discharged, hospital site, month and year of discharge date, and whether each patient’s medication reconciliation for diabetes medications at hospital discharge was in agreement with the inpatient diabetes team’s recommendations. Patients who were discharged on medications discordant from those recommended by the inpatient diabetes service were subcategorized into three groups: 1) one medication incorrect 2) more than one medication incorrect and 3) the primary service did not notify the consult team of patient’s discharge or request final recommendations for diabetes medications prior to discharge. Based on the findings of this study, an educational intervention was implemented in November 2019 to the Hospital Medicine services regarding diabetes discharge medication reconciliation. Results: Of the 3018 patients who received an endocrinology consultation for diabetes at a tertiary university medical center, 2279 patients (76%) were discharged on correct medications, 165 patients (5%) were discharged with one incorrect medication, 443 patients (15%) were discharged with more than one incorrect medication, and 121 patients (4%) were discharged without final discharge recommendations from the diabetes service. There was no significant variation based on discharging service or month of the year. After an educational intervention was implemented in November 2019 to the Hospital Medicine service on the existence and use of a comprehensive diabetes discharge order set, the percentage of patients discharged on correct medications improved to 92% (11/12 patients) compared to prior 81% (44/54 patients). Conclusion: Despite detailed discharge medication recommendations including patient education detailing the recommended regimen by the endocrinology diabetes service, a significant number of patients were discharged by providers across all services on diabetes medications discrepant with the diabetes service’s recommendations. Educational efforts improved the rate of correct medications at discharge on the Hospital Medicine service, and additional educational interventions with other services may be helpful in improving medication reconciliation accuracy.

scholarly journals 210. Vancomycin Infusion Frequency and Intensity: Analysis of Real-World Data Generated from Automated Infusion Devices

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S108-S108
Author(s):  
Cynthia Yamaga ◽  
David L Bostick ◽  
Ying P Tabak ◽  
Ann Liu-Ferrara ◽  
Didier Morel ◽  
...  
Keyword(s):  
Acute Care ◽  
Real World ◽  
Acute Care Hospitals ◽  
Data Driven ◽  
Stopping Times ◽  
Becton Dickinson ◽  
Infusion Device ◽  
Session Duration ◽  
Number Of Patients ◽  
Infusion Devices

Abstract Background Automated infusion devices captures actual infused medication administration data in real-time. Vancomycin use is now recommended to be driven by AUC (area under the curve) dosing. We evaluated automated infusion device data to depict vancomycin administration practices in acute care hospitals. Figure 1. Distribution of vancomycin infusion dosing Figure 2. Distribution of time intervals between each vancomycin infusion session (mostly around 8 or 12 hours) Methods We analyzed archived vancomycin infusion data from 2,417 patients captured by automated infusion systems from 3 acute care hospitals. The infusion device informatics software recorded a variety of events during infusion – starting and stopping times, alarms and alerts, vancomycin dose, and other forms of timestamped usage information. We evaluated infusion session duration and dosing, using data-driven clustering algorithms. Results A total of 13,339 vancomycin infusion sessions from 2,417 unique adult patients were analyzed. Approximately 26.1% of patients had just one infusion of vancomycin. For the rest of the patients, the median number of infusion sessions per patient was 4; the interquartile range was 3 and 8. The most common dose was 1.0 gram (53.7%) or 1.5 gram (24.6%) (see Figure 1). The distribution of infusion session duration (hours) was 4.2% (≤1.0 hh); 40.1% (1.01–1.5 hh); 29.1% (1.51–2.0 hh); and 26.6% (&gt;2.0 hh). The dosing frequency was 39.5% (q8 hh), 42.9% (q12 hh), 11.1% (q24 hh), and 6.5% (&gt;q24 hh) (Figure 2), demonstrating clinical interpretability. Conclusion A considerable number of patients received just one vancomycin infusion during their hospital stay, suggesting a potential overuse of empiric vancomycin. The majority of infusion doses were between 1 to 1.5 grams and most infusion sessions were administered every 8 or 12 hours. The actual infusion duration for each dose often exceeds the prescribed 1- or 2-hour infusion orders, which may be due to known instances of infusion interruptions due to patient movement, procedures or IV access compromise. The data generated by infusion devices can augment insights on actual antimicrobial administration practices and duration. As vancomycin AUC dosing becomes more prevalent, real world infusion data may aid timely data-driven antimicrobial stewardship and patient safety interventions for vancomycin and other AUC dosed drugs. Disclosures Cynthia Yamaga, PharmD, BD (Employee) David L. Bostick, PhD, Becton, Dickinson and Co. (Employee) Ying P. Tabak, PhD, Becton, Dickinson and Co. (Employee) Ann Liu-Ferrara, PhD, Becton, Dickinson and Co. (Employee) Didier Morel, PhD, Becton, Dickinson and Co. (Employee) Kalvin Yu, MD, Becton, Dickinson and Company (Employee)GlaxoSmithKline plc. (Other Financial or Material Support, Funding)

scholarly journals 587. An Intervention to Improve COVID-19 Vaccination Rates Among Inpatients at a Veterans Affairs Hospital

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S396-S396
Author(s):  
Ayako Fujita ◽  
Tiffany Goolsby ◽  
Krista Powell ◽  
Emily J Cartwright
Keyword(s):  
Discharge Planning ◽  
Medical Center ◽  
Pneumococcal Vaccination ◽  
Vaccine Uptake ◽  
Training Period ◽  
Healthcare Personnel ◽  
Severe Illness ◽  
Vaccination Rates ◽  
Period 2 ◽  
Number Of Patients

Abstract Background Hospitalizations are an opportunity to increase vaccine uptake and hospital-based strategies have been effective at increasing influenza and pneumococcal vaccination. Offering COVID-19 vaccination at discharge can reduce barriers to vaccination and target patients at high risk for severe illness and death. We evaluated a COVID-19 vaccine intervention implemented as part of routine discharge planning. Methods We trained healthcare personnel during April 2021 to review and document vaccine eligibility and interest for adult inpatients on medical, surgical, or psychiatric wards at the Atlanta VA Medical Center during discharge planning using a templated note in the electronic medical record (EMR). Outpatient vaccination center personnel were deployed to the participating wards daily (except Sundays) to facilitate vaccine administration at discharge. We measured the percentage of discharged patients with vaccine eligibility documented using the template and compared the number of patients vaccinated at discharge in the 4 weeks pre- and post-training. All Georgia adults became eligible for COVID-19 vaccines on March 25, 2021, prior to our intervention. Results Of the 769 patients discharged from one of the participating wards during the 4-week post-training, 474 (62%) had vaccine eligibility documented (Table 1). Of the 474 patients with documentation, 88 (19%) were eligible. Reasons for ineligibility included prior vaccination (n=266, 69%), patient refusal (n=103, 27%), and acute COVID infection (n=12, 3%). Of the 88 eligible patients, 61 (69%) received vaccination before discharge. In total, 16 of 793 inpatients in the pre-training period and 61 of 769 in the post-training period (2% vs 8%; p&lt; 0.05) were vaccinated prior to discharge. Table 1. COVID-19 vaccine eligibility and vaccination before discharge during the post-training period, reported by week Conclusion We found relatively high and sustained uptake of an intervention to screen hospitalized patients for COVID-19 vaccination eligibility. Creating a templated note in the EMR resulted in vaccination of nearly 70% of eligible patients prior to hospital discharge. Disclosures All Authors: No reported disclosures

scholarly journals Evaluation of the Intensive Care Unit Resources and Utilization in Two Governmental Hospitals in Tripoli, Libya

2019 ◽  
Vol 34 (3) ◽  
pp. 130-136
Author(s):  
Ahmed Atia ◽  
Abdulsalam Ashur ◽  
Hosam Elmahmoudi ◽  
Ahmed Abired ◽  
Nafisa Bkhait
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Medical Center ◽  
Critically Ill Patient ◽  
Surgical Intensive Care Unit ◽  
Mechanical Ventilators ◽  
Surgical Intensive Care ◽  
Data Collection Sheet ◽  
Number Of Patients ◽  
Service Areas

The growing population in Tripoli is projected to have a sustained increase in the demand for health services, especially in-service areas with limited resources such as intensive care units (ICUs). Currently, ICUs in the city of Tripoli routinely operate at or near full capacity and have a limited ability to accommodate the next critically ill patient. This disparity in demand and supply makes a substantial strain on our health care system. In response to this rising problem, the current study aimed to investigate the ICU capacity in the two largest hospitals in Tripoli, Libya. This is a retrospective observational study that conducted to compare ICU capacities and admission in the Medical intensive care unit (MICU) and surgical intensive care unit (SICU) of Tripoli Medical center (TMC) and Alkhadra hospital (AH) in Tripoli city of Libya. ICUs capacity and admissions were assessed and recorded in data collection sheet that includes; type of ICU, number of available ICU beds, number of available functional monitors, number of available functional mechanical ventilators, number of patients admitted to the ICU, and number of ICU nurse. In TMC, MICU occupied with 4 beds, 4 monitors, 3 mechanical ventilators (MV), 5 patients admitted, and 13 nurses. Whereas SICU engaged with 4 beds, 5 monitors, 5 MV, 13 patients admitted and 15 nurses. While MICU at AHT was occupied with 4 beds, 4 monitors, 1 MV, and 4 admitted patients with 1 nurse care, SICU at CHT was comprised of 3 beds, 3 monitors, 0 MV, and 3 patients with 1 nurse stuff. We concluded that facilities at both MICU and SICU at Alkhadra hospital of Tripoli were less efficient than MICU and SICU at Tripoli Medical centre. Both ICUs at AHT had not enough beds, observation equipment, and nursing staff to take care of patients. However, facilities of both ICUs at TMC were also not sufficient.

scholarly journals Patient-to-Patient Transmission ofAcinetobacter baumanniiGastrointestinal Colonization in the Intensive Care Unit

2019 ◽  
Vol 63 (8) ◽  
Author(s):  
Anthony D. Harris ◽  
J. Kristie Johnson ◽  
Lisa Pineles ◽  
Lyndsay M. O’Hara ◽  
Robert A. Bonomo ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Genetic Relatedness ◽  
Medical Center ◽  
Culture Collection ◽  
Pfge Type ◽  
Surgical Intensive Care ◽  
Content Type ◽  
University Of Maryland ◽  
Number Of Patients

ABSTRACTAcinetobacter baumanniiis an important nosocomial pathogen. The objective of this study was to determine the proportion ofA. baumanniiinfections due to patient-to-patient transmission by analyzing the molecular epidemiology of patients who acquiredA. baumannii, using perianal surveillance cultures in a large 2-year intensive care unit (ICU) population. The design was a prospective cohort study. Patients who were admitted to the medical and surgical intensive care units at the University of Maryland Medical Center from 2011 to 2013 underwent admission, weekly, and discharge perianal culture collection. Using multilocus sequence typing (MLST) with subsequent pulsed-field gel electrophoresis (PFGE) for increased discrimination, combined with hospital overlap, the number of patients that acquiredA. baumanniidue to patient-to-patient transmission was determined. Our cohort consisted of 3,452 patients. In total, 196 cohort patients were colonized withA. baumannii; 130 patients were positive at ICU admission, and 66 patients acquiredA. baumanniiduring their stay. Among the 196A. baumanniipatient isolates, there were 91 unique MLST types. Among the 66 patients who acquiredA. baumannii, 31 (50%) were considered genetically related by MLST and/or PFGE type, and 11 (17%) were considered patient-to-patient transmission by genetic relatedness and overlapping hospital stay. Our data show that, of those cases ofA. baumanniiacquisition, at least 17% were cases of patient-to-patient transmission.

Burden and characteristics of influenza A and B in Danish intensive care units during the 2009/10 and 2010/11 influenza seasons

2012 ◽  
Vol 141 (4) ◽  
pp. 767-775 ◽  
Author(s):  
S. GUBBELS ◽  
T. G. KRAUSE ◽  
K. BRAGSTAD ◽  
A. PERNER ◽  
K. MØLBAK ◽  
...  
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Influenza A ◽  
Age Groups ◽  
Influenza Surveillance ◽  
Severe Illness ◽  
Influenza B ◽  
Clinical Parameters ◽  
Number Of Patients ◽  
Influenza A H1n1

SUMMARYInfluenza surveillance in Danish intensive care units (ICUs) was performed during the 2009/10 and 2010/11 influenza seasons to monitor the burden on ICUs. All 44 Danish ICUs reported aggregate data for incidence and point prevalence, and case-based demographical and clinical parameters. Additional data on microbiological testing, vaccination and death were obtained from national registers. Ninety-six patients with influenza A(H1N1)pdm09 were recorded in 2009/10; 106 with influenza A and 42 with influenza B in 2010/11. The mean age of influenza A patients was higher in 2010/11 than in 2009/10, 53 vs. 44 years (P = 0·004). No differences in other demographic and clinical parameters were detected between influenza A and B patients. In conclusion, the number of patients with severe influenza was higher in Denmark during the 2010/11 than the 2009/10 season with a shift towards older age groups in influenza A patients. Influenza B caused severe illness and needs consideration in clinical and public health policy.

PREVALENCE OF CARBAPENEM-RESISTANT ACINETOBACTER BAUMANNII (CRAB) IN MEDICAL AND SURGICAL INTENSIVE CARE UNITS (ICUS) OF JPMC, KARACHI

2019 ◽  
Author(s):  
Rabia Arshad
Keyword(s):  
Intensive Care ◽  
Antimicrobial Resistance ◽  
Acinetobacter Baumannii ◽  
Intensive Care Units ◽  
Carbapenem Resistance ◽  
Chronic Obstructive ◽  
Surgical Intensive Care ◽  
Carbapenem Resistant ◽  
Number Of Patients ◽  
Increased Risk

Background: Antimicrobial resistance is one of the research priorities of health organizations due to increased risk of morbidity and mortality. Outbreaks of nosocomial infections caused by carbapenem-resistant Acinetobacter Baumannii (CRAB) strains are at rise worldwide. Antimicrobial resistance to carbapenems reduces clinical therapeutic choices and frequently led to treatment failure. The aim of our study was to determine the prevalence of carbapenem resistance in A. baumannii isolated from patients in intensive care units (ICUs). Methods: This cross-sectional study was carried out in the Department of Microbiology, Basic Medical Sciences Institute (BMSI), Jinnah Postgraduate Medical Centre (JPMC), Karachi, from December 2016 to November 2017. Total 63 non-repetitive A. baumannii were collected from the patients’ specimens, admitted to medical and surgical ICUs and wards of JPMC, Karachi. The bacterial isolates were processed according to standard microbiological procedures to observe for carbapenem resistance. SPSS 21 was used for data analysis. Results: Out of the 63 patients, 40 (63.5%) were male. The age of the patient ranged from 15-85 year, with average of 43 year. 34.9% patients had been hospitalized for 3 days. Chronic obstructive pulmonary disease was present in highest number with average of 58.7% for morbidity. Number of patients on mechanical ventilation was highest (65.1%). All isolates were susceptible to colistin. The resistance to ampicillin-sulbactam, ceftazidime, ciprofloxacin, amikacin, piperacillin- tazobactam and meropenem was 82.5%, 81%, 100%, 87.3%, 82.5% and 82% respectively. Out of 82% CRAB, 77% were obtained from ICUs. Conclusion: This study has revealed the high rate of carbapenem resistance in A. baumannii isolates in ICUs thus leaving behind limited therapeutic options.

Sign in / Sign up

Export Citation Format

Share Document