scholarly journals Impact of COVID-19 on non-COVID intensive care unit service utilization, case mix and outcomes: A registry-based analysis from India

2021 ◽  
Vol 6 ◽  
pp. 159
Author(s):  
◽  
Neill KJ Adhikari ◽  
Abi Beane ◽  
Dedeepiya Devaprasad ◽  
Robert Fowler ◽  
...  
Keyword(s):  
Critical Care ◽  
Intensive Care ◽  
Service Utilization ◽  
Care Service ◽  
Severity Of Illness ◽  
Apache Ii Score ◽  
Case Mix ◽  
Icu Mortality ◽  
The Impact ◽  
Registry Database

Background: Coronavirus disease 2019 (COVID-19) has been responsible for over 3.4 million deaths globally and over 25 million cases in India. As part of the response, India imposed a nation-wide lockdown and prioritized COVID-19 care in hospitals and intensive care units (ICUs). Leveraging data from the Indian Registry of IntenSive care, we sought to understand the impact of the COVID-19 pandemic on critical care service utilization, case-mix, and clinical outcomes in non-COVID ICUs.  Methods: We included all consecutive patients admitted between 1 st October 2019 and 27 th September 2020. Data were extracted from the registry database and included patients admitted to the non-COVID or general ICUs at each of the sites. Outcomes included measures of resource-availability, utilisation, case-mix, acuity, and demand for ICU beds. We used a Mann-Whitney test to compare the pre-pandemic period (October 2019 - February 2020) to the pandemic period (March-September 2020). In addition, we also compared the period of intense lockdown (March-May 31 st 2020) with the pre-pandemic period. Results: There were 3424 patient encounters in the pre-pandemic period and 3524 encounters in the pandemic period. Comparing these periods, weekly admissions declined (median [Q1 Q3] 160 [145,168] to 113 [98.5,134]; p<0.001); unit turnover declined (median [Q1 Q3] 12.1 [11.32,13] to 8.58 [7.24,10], p<0.001), and APACHE II score increased (median [Q1 Q3] 19 [19,20] to 21 [20,22] ; p<0.001). Unadjusted ICU mortality increased (9.3% to 11.7%, p=0.015) and the length of ICU stay was similar (median [Q1 Q3] 2.11 [2, 2] vs. 2.24 [2, 3] days; p=0.151). Conclusion: Our registry-based analysis of the impact of COVID-19 on non-COVID critical care demonstrates significant disruptions to healthcare utilization during the pandemic and an increase in the severity of illness.

scholarly journals Impact of COVID-19 on non-COVID intensive care unit service utilization, case mix and outcomes: A registry-based analysis from India

2021 ◽  
Vol 6 ◽  
pp. 159
Author(s):  
◽  
Neill KJ Adhikari ◽  
Abi Beane ◽  
Dedeepiya Devaprasad ◽  
Robert Fowler ◽  
...  
Keyword(s):  
Critical Care ◽  
Intensive Care ◽  
Service Utilization ◽  
Care Service ◽  
Severity Of Illness ◽  
Apache Ii Score ◽  
Case Mix ◽  
Icu Mortality ◽  
The Impact ◽  
Registry Database

Background: Coronavirus disease 2019 (COVID-19) has been responsible for over 3.4 million deaths globally and over 25 million cases in India. As part of the response, India imposed a nation-wide lockdown and prioritized COVID-19 care in hospitals and intensive care units (ICUs). Leveraging data from the Indian Registry of IntenSive care, we sought to understand the impact of the COVID-19 pandemic on critical care service utilization, case-mix, and clinical outcomes in non-COVID ICUs.  Methods: We included all consecutive patients admitted between 1st October 2019 and 27th September 2020. Data were extracted from the registry database and included patients admitted to the non-COVID or general ICUs at each of the sites. Outcomes included measures of resource-availability, utilisation, case-mix, acuity, and demand for ICU beds. We used a Mann-Whitney test to compare the pre-pandemic period (October 2019 - February 2020) to the pandemic period (March-September 2020). In addition, we also compared the period of intense lockdown (March-May 31st 2020) with the pre-pandemic period. Results: There were 3424 patient encounters in the pre-pandemic period and 3524 encounters in the pandemic period. Comparing these periods, weekly admissions declined (median [Q1 Q3] 160 [145,168] to 113 [98.5,134]; p=0.00002); unit turnover declined (median [Q1 Q3] 12.1 [11.32,13] to 8.58 [7.24,10], p<0.00001), and APACHE II score increased (median [Q1 Q3] 19 [19,20] to 21 [20,22] ; p<0.00001). Unadjusted ICU mortality increased (9.3% to 11.7%, p=0.01519) and the length of ICU stay was similar (median [Q1 Q3] 2.11 [2, 2] vs. 2.24 [2, 3] days; p=0.15096). Conclusion: Our registry-based analysis of the impact of COVID-19 on non-COVID critical care demonstrates significant disruptions to healthcare utilization during the pandemic and an increase in the severity of illness.

scholarly journals Why the surgical patients are so critical in their intensive care unit arrival?

2013 ◽  
Vol 28 (suppl 1) ◽  
pp. 48-53 ◽  
Author(s):  
Anibal Basile-Filho ◽  
Mayra Gonçalves Menegueti ◽  
Maria Auxiliadora-Martins ◽  
Edson Antonio Nicolini
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Negative Impact ◽  
Survival Rates ◽  
Severity Of Illness ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Death Risk ◽  
Icu Transfer ◽  
The Impact

PURPOSE: To assess the ability of the Acute Physiology and Chronic Health Evaluation II (APACHE II) to stratify the severity of illness and the impact of delay transfer to an Intensive Care Unit (ICU) on the mortality of surgical critically ill patients. METHODS: Five hundred and twenty-nine patients (60.3% males and 39.7% females; mean age of 52.8 ± 18.5 years) admitted to the ICU were retrospectively studied. The patients were divided into survivors (n=365) and nonsurvivors (n=164). APACHE II and death risk were analysed by generation of receiver operating characteristic (ROC) curves. The interval time between referral and ICU arrival was also registered. The level of significance was 0.05. RESULTS: The mean APACHE II and death risk was 19.9 ± 9.6 and 37.7 ± 28.9%, respectively. The area under the ROC curve for APACHE II and death risk was 0.825 (CI = 0.765-0.875) and 0.803 (CI = 0.741-0.856). The overall mortality (31%) increased progressively with the delay time to ICU transfer, as also evidencied by the APACHE II score and death risk. CONCLUSION: This investigation shows that the longer patients wait for ICU transfer the higher is their criticallity upon ICU arrival, with an obvious negative impact on survival rates.

scholarly journals Using clinical databases to evaluate healthcare interventions

2010 ◽  
Vol 26 (1) ◽  
pp. 86-94 ◽  
Author(s):  
Sheila Harvey ◽  
Kathy Rowan ◽  
David Harrison ◽  
Nick Black
Keyword(s):  
Intensive Care ◽  
Hospital Mortality ◽  
Additional Data ◽  
Severity Of Illness ◽  
Case Mix ◽  
University Hospitals ◽  
Clinical Databases ◽  
Baseline Characteristics ◽  
Healthcare Interventions ◽  
The Impact

Objectives: The aim of this study was to test the feasibility of conducting rigorous, nonrandomized studies (NRSs) of healthcare interventions using existing clinical databases in terms of the following: recruiting a large representative sample of hospitals, identifying eligible cases, matching cases to controls to achieve similar baseline characteristics, making meaningful comparisons of outcomes, and carrying out subgroup analyses.Methods: Data were extracted from the Intensive Care National Audit & Research Centre's Case Mix Programme Database to investigate the impact of management with a pulmonary artery catheter (PAC) in intensive care unit (ICU) patients. Participating ICUs were invited to collect additional data for the analysis. Patients managed with a PAC were matched to control patients on their propensity score. Hospital mortality was then compared between the two groups.Results: Of 117 eligible ICUs, 68 (58 percent) agreed to participate, of which 57 (84 percent) collected additional data. Although a slightly higher proportion of larger ICUs in university hospitals participated, the patient case-mix was similar to that in nonparticipating ICUs. Almost all patients managed with a PAC (98 percent) were successfully matched to patients managed without a PAC. The two groups were similar for baseline characteristics. However, hospital mortality was worse for PAC patients than for non-PAC patients (odds ratio, 1.28; 95 percent confidence interval, 1.06–1.55). Subgroup analysis suggested that the impact of management with a PAC was modified by severity of illness.Conclusions: Rigorous NRSs are feasible if they are based on data from high-quality clinical databases. However, the reliability of estimated treatment effects from such studies requires further investigation.

scholarly journals Evaluating service development in critical care: The impact of establishing a medical high dependency unit on intensive care unit workload, case mix, and mortality

2018 ◽  
Vol 19 (3) ◽  
pp. 226-235
Author(s):  
Nabeel Amiruddin ◽  
Gordon J Prescott ◽  
Douglas A Coventry ◽  
Jan O Jansen
Keyword(s):  
Intensive Care Unit ◽  
Time Series ◽  
Critical Care ◽  
Intensive Care ◽  
Interrupted Time Series ◽  
Case Mix ◽  
High Dependency Unit ◽  
Medical Patients ◽  
High Dependency ◽  
The Impact

Background Critical care services underpin the delivery of many types of secondary care, and there is increasing focus on how to best deliver such services. The aim of this study was to investigate the impact of establishing a medical high dependency unit, in a tertiary referral center, on the workload, case mix, and mortality of the intensive care unit. Methods Single-center, 11-year retrospective study of patients admitted to the general intensive care unit, before and after the opening of the medical high dependency unit, using interrupted time series methodology. Results Over the duration of the study period, 3209 medical patients were admitted to the intensive care unit. There was a constant rate of medical admissions to the intensive care unit until the opening of the medical high dependency unit, followed by a statistically significant decline thereafter. There was a statistically significant decrease in the average severity of illness of medical patients prior to the opening of the medical high dependency unit, but there was no evidence of a change following the opening of the unit. There was no evidence of a statistically significant change in the estimated mean standardized mortality ratio for either medical or surgical admissions after the intervention. Conclusions The opening of a medical high dependency unit had a minimal impact on the intensive care unit. There was, in all likelihood, an unmet need—of less seriously ill patients, who were previously looked after on a normal ward, but did not require intensive care unit admission—who are now cared for in the new medical high dependency unit. Interrupted time series analysis, although not without limitations, is a useful mean of evaluating changes in service delivery.

scholarly journals The Impact of Implementing Critical Care Team on Open General Intensive Care Unit

2012 ◽  
Vol 73 (2) ◽  
pp. 100 ◽  
Author(s):  
Ick Hee Kim ◽  
Seung Bae Park ◽  
Seonguk Kim ◽  
Sang-Don Han ◽  
Seung Seok Ki ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Critical Care ◽  
Intensive Care ◽  
Care Team ◽  
General Intensive Care Unit ◽  
The Impact ◽  
Critical Care Team

scholarly journals Improvement of APACHE II score system for disease severity based on XGBoost algorithm

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yan Luo ◽  
Zhiyu Wang ◽  
Cong Wang
Keyword(s):  
Intensive Care ◽  
Hospital Mortality ◽  
Patient Outcomes ◽  
External Validation ◽  
Apache Ii ◽  
Validation Dataset ◽  
Apache Ii Score ◽  
Severity Scores ◽  
The Impact ◽  
Improve Patient

Abstract Background Prognostication is an essential tool for risk adjustment and decision making in the intensive care units (ICUs). In order to improve patient outcomes, we have been trying to develop a more effective model than Acute Physiology and Chronic Health Evaluation (APACHE) II to measure the severity of the patients in ICUs. The aim of the present study was to provide a mortality prediction model for ICUs patients, and to assess its performance relative to prediction based on the APACHE II scoring system. Methods We used the Medical Information Mart for Intensive Care version III (MIMIC-III) database to build our model. After comparing the APACHE II with 6 typical machine learning (ML) methods, the best performing model was screened for external validation on anther independent dataset. Performance measures were calculated using cross-validation to avoid making biased assessments. The primary outcome was hospital mortality. Finally, we used TreeSHAP algorithm to explain the variable relationships in the extreme gradient boosting algorithm (XGBoost) model. Results We picked out 14 variables with 24,777 cases to form our basic data set. When the variables were the same as those contained in the APACHE II, the accuracy of XGBoost (accuracy: 0.858) was higher than that of APACHE II (accuracy: 0.742) and other algorithms. In addition, it exhibited better calibration properties than other methods, the result in the area under the ROC curve (AUC: 0.76). we then expand the variable set by adding five new variables to improve the performance of our model. The accuracy, precision, recall, F1, and AUC of the XGBoost model increased, and were still higher than other models (0.866, 0.853, 0.870, 0.845, and 0.81, respectively). On the external validation dataset, the AUC was 0.79 and calibration properties were good. Conclusions As compared to conventional severity scores APACHE II, our XGBoost proposal offers improved performance for predicting hospital mortality in ICUs patients. Furthermore, the TreeSHAP can help to enhance the understanding of our model by providing detailed insights into the impact of different features on the disease risk. In sum, our model could help clinicians determine prognosis and improve patient outcomes.

scholarly journals Characteristics of Patients Using Different Patient Portal Functions and the Impact on Primary Care Service Utilization and Appointment Adherence: Retrospective Observational Study

10.2196/14410 ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. e14410 ◽  
Author(s):  
Xiang Zhong ◽  
Jaeyoung Park ◽  
Muxuan Liang ◽  
Fangyun Shi ◽  
Pamela R Budd ◽  
...  
Keyword(s):  
Primary Care ◽  
Service Utilization ◽  
Care Delivery ◽  
Care Service ◽  
Primary Care Service ◽  
Patient Portal ◽  
Patient Portals ◽  
Appointment Adherence ◽  
Primary Care Patients ◽  
The Impact

Background Patient portals are now widely available and increasingly adopted by patients and providers. Despite the growing research interest in patient portal adoption, there is a lack of follow-up studies describing the following: whether patients use portals actively; how frequently they use distinct portal functions; and, consequently, what the effects of using them are, the understanding of which is paramount to maximizing the potential of patient portals to enhance care delivery. Objective To investigate the characteristics of primary care patients using different patient portal functions and the impact of various portal usage behaviors on patients’ primary care service utilization and appointment adherence. Methods A retrospective, observational study using a large dataset of 46,544 primary care patients from University of Florida Health was conducted. Patient portal users were defined as patients who adopted a portal, and adoption was defined as the status that a portal account was opened and kept activated during the study period. Then, users were further classified into different user subgroups based on their portal usage of messaging, laboratory, appointment, and medication functions. The intervention outcomes were the rates of primary care office visits categorized as arrived, telephone encounters, cancellations, and no-shows per quarter as the measures of primary care service utilization and appointment adherence. Generalized linear models with a panel difference-in-differences study design were then developed to estimate the rate ratios between the users and the matched nonusers of the four measurements with an observational window of up to 10 quarters after portal adoption. Results Interestingly, a high propensity to adopt patient portals does not necessarily imply more frequent use of portals. In particular, the number of active health problems one had was significantly negatively associated with portal adoption (odds ratios [ORs] 0.57-0.86, 95% CIs 0.51-0.94, all P<.001) but was positively associated with portal usage (ORs 1.37-1.76, 95% CIs 1.11-2.22, all P≤.01). The same was true for being enrolled in Medicare for portal adoption (OR 0.47, 95% CI 0.41-0.54, P<.001) and message usage (OR 1.44, 95% CI 1.03-2.03, P=.04). On the impact of portal usage, the effects were time-dependent and specific to the user subgroup. The most salient change was the improvement in appointment adherence, and patients who used messaging and laboratory functions more often exhibited a larger reduction in no-shows compared to other user subgroups. Conclusions Patients differ in their portal adoption and usage behaviors, and the portal usage effects are heterogeneous and dynamic. However, there exists a lack of match in the patient portal market where patients who benefit the most from patient portals are not active portal adopters. Our findings suggest that health care delivery planners and administrators should remove the barriers of adoption for the portal beneficiaries; in addition, they should incorporate the impact of portal usage into care coordination and workflow design, ultimately aligning patients’ and providers’ needs and functionalities to effectively deliver patient-centric care.

scholarly journals Serum potassium levels and outcomes in critically ill patients in the medical intensive care unit

2018 ◽  
Vol 46 (3) ◽  
pp. 1254-1262 ◽  
Author(s):  
Surat Tongyoo ◽  
Tanuwong Viarasilpa ◽  
Chairat Permpikul
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Critically Ill ◽  
Serum Potassium ◽  
Critically Ill Patients ◽  
Medical Intensive Care Unit ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Icu Mortality ◽  
Medical Intensive Care

Objective To compare the outcomes of patients with and without a mean serum potassium (K+) level within the recommended range (3.5–4.5 mEq/L). Methods This prospective cohort study involved patients admitted to the medical intensive care unit (ICU) of Siriraj Hospital from May 2012 to February 2013. The patients’ baseline characteristics, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, serum K+ level, and hospital outcomes were recorded. Patients with a mean K+ level of 3.5 to 4.5 mEq/L and with all individual K+ values of 3.0 to 5.0 mEq/L were allocated to the normal K+ group. The remaining patients were allocated to the abnormal K+ group. Results In total, 160 patients were included. Their mean age was 59.3±18.3 years, and their mean APACHE II score was 21.8±14.0. The normal K+ group comprised 74 (46.3%) patients. The abnormal K+ group had a significantly higher mean APACHE II score, proportion of coronary artery disease, and rate of vasopressor treatment. An abnormal serum K+ level was associated with significantly higher ICU mortality and incidence of ventricular fibrillation. Conclusion Critically ill patients with abnormal K+ levels had a higher incidence of ventricular arrhythmia and ICU mortality than patients with normal K+ levels.

scholarly journals 1017. Scratching the Surface with NHSN Data: Defining High Impact Stewardship Targets in Critical Care

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S357-S358
Author(s):  
Kelsie Cowman ◽  
Victor Chen ◽  
Nidhi Saraiya ◽  
Yi Guo ◽  
Rachel Bartash ◽  
...  
Keyword(s):  
Critical Care ◽  
Intensive Care ◽  
Antibiotic Use ◽  
Time Frame ◽  
High Impact ◽  
Chart Audit ◽  
Surgical Intensive Care ◽  
Patient Level ◽  
Clostridioides Difficile ◽  
The Impact

Abstract Background The National Healthcare Safety Network (NHSN) provides risk-adjusted Standardized Antimicrobial Administration Ratios (SAAR) as a benchmark for medical and surgical intensive care units (ICU). Antibiotic use (AU) data does not provide patient-level information (e.g., antibiotic appropriateness, indications, durations, etc.). However, we hypothesize that AU data can help define high impact stewardship targets, particularly in the context of critical care Clostridioides difficile rates. Methods Units with high rates of AU and hospital-onset (HO) C. difficile were selected for review. A monthly AU and C. difficile dashboard was created for ICU providers, inclusive of data from May 2018 onwards. We also performed chart audits for indication, duration, and location of initiation for all medical intensive care unit (MICU) patients receiving piperacillin/tazobactam (P/T) or vancomycin (Van) during February 2019 per request of ICU stakeholders. Data were used to obtain stewardship buy-in from local MICU champions. Results AU data indicated that (1) all 3 MICUs consistently had SAARs >1 for broad-spectrum categories and (2) Van and P/T were the highest volume agents on these units (Figure 1). Chart audit of 135 MICU patients showed that 17 patients received P/T, 34 Van, and 84 (62%) both agents; median duration was 2 days for Van and 3 days for P/T (Figure 2). Approximately half of initiations occurred in the emergency department (ED) (50% Van, 47% P/T); most common indications were “respiratory tract infection” and “severe sepsis/septic shock” for both P/T (77%) and Van (74%) (Figure 2). HO C. difficile in MICUs accounted for 6%, 13%, and 16% of total HO C. difficile cases in campuses A, B, and C, respectively during the time frame (Figure 1). Conclusion We feel that NHSN data scratches the surface of the deep-rooted challenges of ICU stewardship. However, it can identify AU trends and most frequently prescribed antibiotics in the context of unit-specific C. difficile rates. Intensive stewardship audit can further uncover areas for intervention, such as ED Van and P/T overprescribing. We suggest presenting clinical stakeholders with a quarterly “stewardship dashboard” combining AU rates, patient-level data, and C. difficile rates to maximize the impact of stewardship endeavors. Disclosures All authors: No reported disclosures.

scholarly journals The Impact of Changes to an Electronic Admission Order Set on Prescribing and Clinical Outcomes in the Intensive Care Unit

2020 ◽  
Vol 11 (01) ◽  
pp. 182-189
Author(s):  
Ellen T. Muniga ◽  
Todd A. Walroth ◽  
Natalie C. Washburn
Keyword(s):  
Intensive Care Unit ◽  
Critical Care ◽  
Intensive Care ◽  
Clinical Outcomes ◽  
Prescribing Patterns ◽  
Number Of Patients ◽  
Order Sets ◽  
Critical Care Admission ◽  
The Impact ◽  
Order Set

Abstract Background Implementation of disease-specific order sets has improved compliance with standards of care for a variety of diseases. Evidence of the impact admission order sets can have on care is limited. Objective The main purpose of this article is to evaluate the impact of changes made to an electronic critical care admission order set on provider prescribing patterns and clinical outcomes. Methods A retrospective, observational before-and-after exploratory study was performed on adult patients admitted to the medical intensive care unit using the Inpatient Critical Care Admission Order Set. The primary outcome measure was the percentage change in the number of orders for scheduled acetaminophen, a histamine-2 receptor antagonist (H2RA), and lactated ringers at admission before implementation of the revised order set compared with after implementation. Secondary outcomes assessed clinical impact of changes made to the order set. Results The addition of a different dosing strategy for a medication already available on the order set (scheduled acetaminophen vs. as needed acetaminophen) had no impact on physician prescribing (0 vs. 0%, p = 1.000). The addition of a new medication class (an H2RA) to the order set significantly increased the number of patients prescribed an H2RA for stress ulcer prophylaxis (0 vs. 20%, p < 0.001). Rearranging the list of maintenance intravenous fluids to make lactated ringers the first fluid option in place of normal saline significantly decreased the number of orders for lactated ringers (17 vs. 4%, p = 0.005). The order set changes had no significant impact on clinical outcomes such as incidence of transaminitis, gastrointestinal bleed, and acute kidney injury. Conclusion Making changes to an admission order set can impact provider prescribing patterns. The type of change made to the order set, in addition to the specific medication changed, may have an effect on how influential the changes are on prescribing patterns.

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