Predictors Of Admission Research Articles

Pediatric Fracture Epidemiology and US Emergency Department Resource Utilization.

Fractures are common childhood injuries that result in emergency department (ED) visits. National trends in pediatric fracture epidemiology and resource utilization are not well described. Our objective is to analyze national trends in pediatric fracture epidemiology, ED disposition, and ED resource utilization from 2010 to 2015. This is an epidemiological study of fracture care in US EDs from 2010 to 2015 for children 0 to 18 years old using the Nationwide Emergency Department Sample. We calculated frequencies and national rates using weighted analyses and census data. We used the test for linear trend to analyze incidence, hospital admission, transfer, and procedural sedation over time. Multivariate logistic regression analyses identified encounter- and hospital-level predictors of transfer, admission, operative care, and use of procedural sedation. During the study period, from 2010 to 2015, a total of 5,398,827 children received ED care for fractures. The pediatric fracture rate was 11.5 ED visits/1000 persons (95% confidence interval [CI], 10.6-12.5) and decreased over time. The admission rate for pediatric fracture patients was 5% and stable over time. The transfer rate increased from 3.3 to 4.1/100 fracture visits (linear trend: odds ratio, 1.06; 95% CI, 1.03-1.09). Utilization of procedural sedation increased from 1.5% to 2.9% of fracture visits (linear trend: odds ratio, 1.17; 95% CI, 1.09-1.25). Predictors associated with disposition and resource utilization include patient age, fracture location, insurance type, hospital type, and region. The national incidence rate of pediatric fractures decreased slightly. Emergency department resource utilization increased over time. With high national volume, understanding pediatric fracture epidemiology and resource utilization is important to the health care system.

Evolving spectrum but persistent high mortality of COVID-19 among patients on kidney replacement therapy in the vaccine era: the Spanish COVID-19 KRT Registry.

ABSTRACTBackgroundKidney replacement therapy (KRT) conferred a high risk for coronavirus disease 2019 (COVID-19) related mortality early in the pandemic. We evaluate the presentation, treatment and outcomes of COVID-19 in patients on KRT over time during the pandemic.MethodsThis registry-based study involved 6080 dialysis and kidney transplant (KT) patients with COVID-19, representing roughly 10% of total Spanish KRT patients. Epidemiology, comorbidity, infection, vaccine status and treatment data were recorded, and predictors of hospital admission, intensive care unit (ICU) admission and mortality were evaluated.ResultsVaccine introduction decreased the number of COVID-19 cases from 1747 to 280 per wave. Of 3856 (64%) COVID-19 KRT patients admitted to the hospital, 1481/3856 (38%) were admitted during the first of six waves. Independent predictors for admission included KT and the first wave. During follow-up, 1207 patients (21%) died, 500/1207 (41%) during the first wave. Among vaccinated patients, mortality was 19%, mostly affecting KT recipients. Overall, independent predictors for mortality were older age, disease severity (lymphopaenia, pneumonia) and ICU rejection. Among patient factors, older age, male sex, diabetes, KT and no angiotensin receptor blockers (ARB) were independent predictors of death. In KT recipients, individual immunosuppressants were independent predictors of death. Over time, patient characteristics evolved and in later pandemic waves, COVID-19 was mainly diagnosed in vaccinated KT recipients; in the few unvaccinated dialysis patients, ICU admissions increased and mortality decreased (28% for the first wave and 16–22% thereafter).ConclusionsThe clinical presentation and outcomes of COVID-19 during the first wave no longer represent COVID-19 in KRT patients, as the pandemic has become centred around vaccinated KT recipients. Vaccines lowered the incidence of diagnosed COVID-19 and mortality. However, mortality remains high despite increased access to ICU care.

P wave peak time and P wave dispersion in severe COVID-19 infection.

With this study, we aimed at evaluating the association between electrocardiographic P wave indices and the severity of COVID-19 infection indicated as intensive care unit (ICU) admission. We included 247 patients who were hospitalized with a diagnosis of COVID-19 infection and underwent 12 lead standard Electrocardiography (ECG). P wave indices, P wave dispersion (Pdis), P wave peak time in V1 lead (PWPTV1), and D2 lead (PWPTD2) were measured using admission ECG. Comparisons were performed between ICU admitting and non-ICU admitting patients. 160 patients were hospitalized in normal wards, and 87 patients were admitted to ICU. Pdis, PWPTV1, and PWPTD2 were prolonged in ICU admitted patients compared with the normal ward admitted patients [40 (30-50) ms vs. 50 (40-55) ms; p<0.001, 61±9 ms vs. 68±9 ms; p<0.001, and 55±7 ms vs. 64±7 ms; p<0.001, respectively]. In multiple logistic regression analysis, PWPTV1 and PWPTD2 were independent predictors of ICU admission. A cut-off point of 67.5 ms PWPTv1 has a sensitivity of 62.1% and a specificity of 69.4% (AUC=0.710, 95% CI: 0.642-0.777, p<0.001) and a cut-off point of 62.5 ms PWPTD2 has a sensitivity of 60.9% and a specificity of 83.6% (AUC=0.819, 95% CI: 0.777-0.871, p<0.001). Admission ECG atrial indices Pdis and PWPT were associated with intensive care unit admission in newly diagnosed COVID-19 patients.

Rates and determinants of involuntary hospitalizations in Athens region: Are there any differences between psychiatric and general hospitals?

Involuntary hospitalizations have come to the fore due to growing concerns that Europe may be in the throes of reinstitutionalization. In Greece, the topic is of sheer importance in light of the incomplete deinstitutionalization process. In this context, the aim of the study is to explore differences in the rates and determinants of involuntary hospitalizations between a psychiatric and a general hospital. A total of 600 patients participated in the study. Information about their socio-demographic and clinical characteristics as well as previous contact with mental health services was garnered from their clinical record and through patient and physician interviews. Symptom severity was assessed with HoNOS. Overall, 60.7% of admissions were involuntary: 63.1% in the psychiatric hospital and 52.5% in the general hospital. In the general hospital, number of previous involuntary admissions and level of aggression constituted risk factors for detention; whereas previous hospitalization amid the last month was the only protective factor. In the psychiatric hospital, no contact with mental health services during the last month was the only risk factor for detention and suicidality the only protective. There is imperative need to design interventions geared towards curbing compulsory admissions. Psychiatric hospitals and other contextual factors should be prioritized. • Compulsory admissions are higher in psychiatric hospitals than in general ones. • The predictors of compulsory admissions were different in the two settings. • Socio-demographic factors did not predict compulsory admissions. • Contextual factors play a prominent role in involuntary hospitalizations. • Aggression and suicidality were also found to play a role in compulsory admissions.

Prognostic accuracy of inflammatory markers in predicting risk of ICU admission for COVID-19: application of time-dependent receiver operating characteristic curves.

ObjectiveIntensive care unit (ICU) admission occurs at different times during hospitalization among patients with COVID-19. We aimed to evaluate the time-dependent receive operating characteristic (ROC) curve and area under the ROC curve, AUC(t), and accuracy of baseline levels of inflammatory markers C-reactive protein (CRP) and neutrophil-to-lymphocyte ratio (NLR) in predicting time to an ICU admission event in patients with severe COVID-19 infection.MethodsIn this observational study, we evaluated 724 patients with confirmed severe COVID-19 referred to Ayatollah Rohani Hospital, affiliated with Babol University of Medical Sciences, Iran.ResultsThe AUC(t) of CRP and NLR reached 0.741 (95% confidence interval [CI]: 0.661–0.820) and 0.690 (95% CI: 0.607–0.772), respectively, in the first 3 days after hospital admission. The optimal cutoff values of CRP and NLR for stratification of ICU admission outcomes in patients with severe COVID-19 were 78 mg/L and 5.13, respectively. The risk of ICU admission was significantly greater for patients with these cutoff values (CRP hazard ratio = 2.98; 95% CI: 1.58–5.62; NLR hazard ratio = 2.90; 95% CI: 1.45–5.77).ConclusionsUsing time-dependent ROC curves, CRP and NLR values at hospital admission were important predictors of ICU admission. This approach is more efficient than using standard ROC curves.

Platelet Distribution Width and Increased D-Dimer at Admission Predicts Subsequent Development of ARDS in COVID-19 Patients.

In the current pandemic of coronavirus disease (COVID-19), the identification of the patients admitted with severe infection–who are disposed to a high risk of acute respiratory distress syndrome (ARDS) development, is of a major significance for the determination of the appropriate therapeutic strategy. Laboratory records in admission were retrospectively reviewed from 493 cases of severe COVID-19 divided into two groups: Group 1 with ARDS and Group 2 without ARDS. The platelet distribution width (PDW) difference between Group 1 and Group 2 is significant–15.10 ± 2.08 fl for those who developed ARDS versus 12.94 ± 2.12 fl for those without ARDS. The sensitivity and the specificity of this parameter is lower than that of D-dimer. After grouping of the PDW values into intervals and combining them with the rate of increase in D-dimer (D-PDWf index) to form a forecasting index, a significant increase in the specificity and sensitivity of the two parameters is identified–area under the ROC curve (AUC) is 0.874 for D-PDWf index, with respective AUC for PDW 0.768 and AUC for D-dimer 0.777. Conclusion: PDW is a significant predictive parameter at admission for subsequent development of ARDS in patients, with increased significance in combination with the degree of increase in D-dimer.

Clinical utility of inflammatory biomarkers in COVID-19 in direct comparison to other respiratory infections-A prospective cohort study.

Inflammatory biomarkers are associated with severity of coronavirus disease 2019 (COVID-19). However, direct comparisons of their utility in COVID-19 versus other respiratory infections are largely missing. We aimed to investigate the prognostic utility of various inflammatory biomarkers in COVID-19 compared to patients with other respiratory infections. Patients presenting to the emergency department with symptoms suggestive of COVID-19 were prospectively enrolled. Levels of Interleukin-6 (IL-6), c-reactive protein (CRP), procalcitonin, ferritin, and leukocytes were compared between COVID-19, other viral respiratory infections, and bacterial pneumonia. Primary outcome was the need for hospitalisation, secondary outcome was the composite of intensive care unit (ICU) admission or death at 30 days. Among 514 patients with confirmed respiratory infections, 191 (37%) were diagnosed with COVID-19, 227 (44%) with another viral respiratory infection (viral controls), and 96 (19%) with bacterial pneumonia (bacterial controls). All inflammatory biomarkers differed significantly between diagnoses and were numerically higher in hospitalized patients, regardless of diagnoses. Discriminative accuracy for hospitalisation was highest for IL-6 and CRP in all three diagnoses (in COVID-19, area under the curve (AUC) for IL-6 0.899 [95%CI 0.850-0.948]; AUC for CRP 0.922 [95%CI 0.879-0.964]). Similarly, IL-6 and CRP ranged among the strongest predictors for ICU admission or death at 30 days in COVID-19 (AUC for IL-6 0.794 [95%CI 0.694-0.894]; AUC for CRP 0.807 [95%CI 0.721-0.893]) and both controls. Predictive values of inflammatory biomarkers were generally higher in COVID-19 than in controls. In patients with COVID-19 and other respiratory infections, inflammatory biomarkers harbour strong prognostic information, particularly IL-6 and CRP. Their routine use may support early management decisions.

Sex Differences in Left Ventricular Assist Device-related Emergency Department Encounters in the United States

BackgroundThere is a paucity of data regarding sex differences in the profiles and outcomes of ambulatory patients on left ventricular assist device (LVAD) support who present to the emergency department (ED). Methods and ResultsWe performed a retrospective analysis of 57,200 LVAD-related ED patient encounters from the 2010 to 2018 Nationwide Emergency Department Sample. International Classification of Diseases Clinical Modification, Ninth Revision and Tenth Revision, codes identified patients aged 18 years or older with LVADs and associated primary and comorbidity diagnoses. Clinical characteristics and outcomes were stratified by sex and compared. Multivariable logistic regression was used to evaluate predictors of hospital admission and death. Female patient encounters comprised 27.2% of ED visits and occurred at younger ages and more frequently with obesity and depression (all P < .01). There were no sex differences in presentation for device complication, stroke, infection, or heart failure (all P > .05); however, female patient encounters were more often respiratory- and genitourinary or gynecological related (both P < .01). After adjustment for age group, diabetes, depression, and hypertension, male patient encounters had a 38% increased odds of hospital admission (95% confidence interval 1.20–1.58), but there was no sex difference in the adjusted odds of death (odds ratio 1.11, 95% confidence interval 0.86–1.45). ConclusionsPatient encounters of females on LVAD support have significantly different comorbidities and outcomes compared with males. Further inquiry into these sex differences is imperative to improve long-term outcomes.

A Machine Learning Algorithm Outperforms Traditional Multiple Regression to Predict Risk of Unplanned Overnight Stay Following Outpatient Medial Patellofemoral Ligament Reconstruction

PurposeTo determine whether conventional logistic regression or machine learning algorithms were more precise in identifying the risk factors for unplanned overnight admission after medial patellofemoral ligament (MPFL) reconstruction.MethodsA retrospective review of the prospectively collected National Surgical Quality Improvement Program database was performed to identify patients who underwent outpatient MPFL reconstruction from 2006–2018. Patients admitted overnight were identified as those with length of stay of 1 or more days. Models were generated using random forest, extreme gradient boosting, adaptive boosting, or elastic net penalized logistic regression, and an additional model was produced as a weighted ensemble of the 4 final algorithms. The predictive capacity of these models was compared to that of logistic regression.ResultsOf the 1307 patients identified, 221 (16.9%) required at least one overnight stay after MPFL reconstruction. Multivariate logistic regression found the following variables to be predictors of inpatient admission: age (odds ratio [OR] = 1.03 [95% confidence interval {CI} 1.02-1.04]; P <.001), spinal anesthesia (OR = 3.42 [95% CI 1.98-6.08]; P < .001), American Society of Anesthesiologists (ASA) class 3/4 (OR = 1.96 [95% CI 1.25-3.06]; P < .001), history of chronic obstructive pulmonary disease (COPD) (OR = 6.44 [95% CI 1.58-26.17]; P = .02), and body mass index (BMI) (OR = 1.03 [95% CI 1.01-1.05]; P < .001). The ensemble model achieved the best performance based on discrimination assessed via internal validation (area under the curve = 0.722). The variables determined most important by the ensemble model were increasing BMI, increasing age, ASA class, anesthesia, smoking, hypertension, lateral release, and history of COPD.ConclusionsAn internally validated machine learning algorithm outperformed logistic regression modeling in predicting the need for unplanned overnight hospitalization after MPFL reconstruction. In this model, the most significant risk factors for admission were age, BMI, ASA class, smoking status, hypertension, lateral release, and history of COPD. This tool can be deployed to augment provider assessment to identify high-risk candidates and appropriately set postoperative expectations for patients.Clinical RelevanceIdentifying and mitigating patient risk factors to prevent adverse surgical outcomes and hospitalizations is one of our primary goals. There may be a key role for machine learning algorithms to help successfully and efficiently risk stratify patients to decrease costs, appropriately set postoperative expectations, and increase the quality of delivered care.

Frailty as a Predictor of Poor Rehabilitation Outcomes among Older Patients Attending a Geriatric Day Hospital Program: An Observational Study.

Background: The Geriatric Day Hospital (GDH) is an important outpatient geriatric service, but there are few data on the role of frailty as a potential predictor of poor outcomes in this setting. Methods: Data were analyzed from 499 patients aged ≥ 60 years attending a 12-week GDH program between 2018 and 2021. Frailty status was defined as non-frail (68, 13.6%), mild/moderate frailty (351, 70.3%), and severe frailty (80, 16.0%) based on the Clinical Frailty Scale (CFS). Outcomes were defined as (1) poor outcome (hospital readmission, death, or medical deterioration) during the program and (2) admission to permanent nursing home care upon completion of the program. Multivariate logistic models were used for predictive analyses. Results: The mean age was 80.3 (standard deviation 7.0); 58.3% were women. Overall, 77 patients (15.4%) had a poor outcome, and 48 (9.6%) were admitted to permanent nursing home care. Poor outcome was experienced by none of the non-frail patients (0%), by 49 (14.0%) patients with mild/moderate frailty, and 22 (27.5%) patients with severe frailty (adjusted OR, 2.0; 95% CI 1.3, 3.2; p < 0.01). Admission to a permanent nursing home care was experienced by none of the non-frail patients (0%), 20 (5.7%) of those with mild/moderate frailty, and 28 (35.0%) with severe frailty (adjusted OR, 2.9; 95% CI 1.3, 6.3; p < 0.01). Conclusions: The CFS is a promising risk predictor of poor outcome and admission to permanent nursing home discharge among older patients attending a GDH program.

Same-day Hospital Discharge after Minimally Invasive Hysterectomy in a Gynecologic Oncology Practice: Feasibility, Safety, Predictors of Admission, and Adverse Outcomes

(1) Determine the feasibility and safety of same-day hospital discharge (SDHD) after minimally invasive hysterectomy (MIH) in a gynecologic oncology practice and (2) detail predictors of immediate postoperative hospital admission and multiple 30-day adverse outcomes. Retrospective cohort study. University of Pittsburgh Medical Center Magee-Womens Hospital. MIH by a gynecologic oncologist between January 2017 and July 2019. Clinicopathologic, operative, and medical characteristics, as well as 30-day postoperative complications, emergency department (ED) encounters, and hospital readmissions were extracted. Admitted and SDHD patients were compared using descriptive, chi-square, Fisher's exact, t test, and logistic regression analyses. Univariate and multivariable analyses (MVA) revealed predictors of postoperative hospital admission, 30-day readmission, and a 30-day composite adverse event variable (all-reported postoperative complications, ED encounter, and/or readmission). A total of 1124 patients were identified, of which 77.3% had cancer or precancer; 775 patients (69.0%) underwent SDHD. On MVA, predictors of postoperative admission included older age, distance from hospital, longer procedure length, operative complications, start time after 2 PM, radical hysterectomy, minilaparotomy, adhesiolysis, cardiac disease, cerebrovascular disease, venous thromboembolism, diabetes, and neurologic disorders (p <.05). Moreover, 30-day adverse outcomes were rare (complication 8.7% National Surgical Quality Improvement Program/11.9% all-reported; ED encounter 5.0%; readmission 3.6%). SDHD patients had fewer all-reported complications (10.3% vs 15.5%, p=.01), no difference in ED encounters (4.6% vs 5.7%, p=.44), and fewer observed readmissions (2.8% vs 5.2%, p=.05). Predictors of readmission were identified on univariate; MVA was not feasible given the low number of events. Longer procedure length and cardiac and obstructive pulmonary disease were predictors of the composite adverse event variable (p <.05). SDHD is feasible and safe after MIH within a representative gynecologic oncology practice. Clinicopathologic, medical, and surgical predictors of multiple adverse outcomes were comprehensively described. By identifying patients at high risk of postoperative adverse events, we can direct SDHD selection in the absence of standardized institutional and/or national consensus guidelines and identify patients for prehabilitation and increased perioperative support.

Improving the Characterization of Stage A and B Heart Failure by Adding Global Longitudinal Strain

BackgroundCurrent guidelines distinguish stage B heart failure (SBHF) (asymptomatic left ventricular [LV] dysfunction) from stage A heart failure (SAHF) (asymptomatic with heart failure [HF] risk factors) on the basis of myocardial infarction, LV remodeling (hypertrophy or reduced ejection fraction [EF]) or valvular disease. However, subclinical HF with preserved EF may not be identified with these criteria. ObjectivesThe purpose of this study was to assess the prediction of incident HF with global longitudinal strain (GLS) in patients with SAHF and SBHF. MethodsThe authors analyzed echocardiograms (including GLS) in 447 patients (age 65 ± 11 years; 77% male) enrolled in a prospective study of HF in individuals at risk of incident HF, with normal or mildly impaired EF (≥40%). Long-term follow-up was obtained via data linkage. Analysis was performed using a competing risks model. ResultsAfter a median of 9 years of follow-up, 50 (10%) of the 447 patients had new HF admissions, and 87 (18%) died. In multivariable analysis, all imaging variables were independent predictors of HF admissions, including left ventricular ejection fraction (LVEF) (HR: 0.97 [95% CI: 0.94-0.99]), LV mass index (HR: 1.01 [95% CI: 1.00-1.02]), left atrial volume index (HR: 1.02 [95% CI: 1.00-1.05]), and E/e′ (HR: 1.05 [95% CI: 1.01-1.24]), incremental to clinical variables (age and Charlson comorbidity score). However, the addition of GLS provided value incremental to both clinical and other echocardiographic parameters (P = 0.004). Impaired GLS (<18%) (HR: 4.09 [95% CI: 1.87-8.92]) was independent and incremental to all clinical and other echocardiographic variables in predicting HF, and impaired LVEF, left ventricular hypertrophy, left atrial enlargement, high E/e′, or SBHF were not predictive. ConclusionsThe inclusion of GLS as a criterion for SBHF would add independent and incremental information to standard markers of SBHF for the prediction of subsequent HF admissions.

The effect of hypertension and antihypertensive therapies on the course of COVID-19 infection: Turkish national health system data

ABSTRACT Aim The effect of hypertension (HT) and antihypertensive therapies such as renin-angiotensin-aldosterone system (RAAS) blockers on the disease course in COVID-19 patients is controversial. The purpose of this study was to evaluate the effect of HT and antihypertensive therapies on the course of COVID-19 disease. Method The age, sex, comorbid diseases, and antihypertensive therapies of 132,790 patients with positive COVID-19 real-time transcriptase polymerase chain reaction (RT-PCR) tests in the Turkish Health Ministry National COVID-19 database between 11 March and 31 May 2020, were examined and analyzed. Results Forty-one percent of the 132,790 patients in this study (median age: 40, 47.3% female) were hospitalized for treatment, and 4.5% were followed-up in the intensive care unit (ICU). The most frequent comorbid disease, at 19.5%, was HT (n = 25,863). Mortality was determined in 4.9% of HT patients and 1.9% of non-HT patients (p < .001). HT, age, and male gender emerged as independent predictors of hospitalization and admission to the ICU, while HT was not a predictor of mortality. In addition, no adverse effect of any antihypertensive treatment, including RAAS inhibitors, on mortality was detected. Conclusion Based on Turkish national data, HT is common in COVID-19 patients, but does not appear to be an independent predictor of mortality, and no adverse effect of RAAS inhibitors on COVID-19-related mortality was observed.

MO671: Should We Undergo Peritoneal Dialysis in Malnourished Chronic Kidney Disease Patients?

Abstract BACKGROUND AND AIMS The implementation of peritoneal dialysis (PD) in end-stage renal disease has been associated with various long-term pathophysiologic changes, which result in adverse consequences, such as malnutrition, occurring in 30–50% of patients. Specific factors to PD might be associated with poor oral intake as continuous absorption of glucose from PD solutions and abdominal fullness induced by the dialysate can suppress appetite. There are several accepted strategies for stratifying malnutrition in PD, such as laboratory markers [serum albumin and normalized protein catabolic ratio (nPCR)] and the recent use of bioimpedance spectroscopy (BIS) (with low lean tissue index). We aimed to evaluate if PD patients with a poor nutritional status at the beginning of the technique had worse outcomes (peritonitis, PD failure, hospitalizations, and death). METHOD We studied a cohort of incident patients from a single PD unit between 1 July 2011 and 31 August 2021, who had one BIS measurement and a peritoneal equilibration test in the first 3 months after the start of PD. We also collect laboratory data within 6 months from the BIS evaluation (serum albumin). Besides demographic parameters, we evaluated established endpoints (peritonitis, PD failure, hospitalizations and death). RESULTS There were included 91 PD patients. The mean age of patients was 65.40 $ \pm $ 13.88 years with a median dialysis vintage of 24 (IQR 14–31.61) months; 59.3% were male and 28% had diabetes. During the follow-up period, 5.5% of the cohort died, 56% were hospitalized and 47.3% had at least one episode of peritonitis. Furthermore, the transfer to hemodialysis occurred in 18 cases (19.8%) and 15 (16.5%) received a renal transplant. There was no difference in nutritional markers median in patients that had clinical endpoints versus those who had not (Table 1). In a multivariable analysis, neither nPCR, nor albumin nor lean tissue index were predictors of peritonitis, PD failure or hospital admissions. CONCLUSION In this study, we found that malnourished patients, prevalent in PD, did not have worse clinical endpoints (higher rate of infections, hospital admissions and PD failure). Although adequate nutrition has been deemed vital to the success of PD, this modality of renal replacement therapy did not result in worse outcomes, despite the malnutrition markers at the beginning of PD. Further studies are warranted to evaluate the positive impact of PD in nutrition status.

C-20 | CT Predictors of Acute Decompensation and ICU Admission in Acute Pulmonary Embolism

Risk stratification models for acute pulmonary embolism lack significant positive predictive value in identifying patients at high risk for early PE-related mortality and need for ICU admission. Our study aims to evaluate CT parameters in acute PE that predicts ICU admission.

PD19-08 SYSTEMIC INFLAMMATORY RESPONSE SYNDROME AND QUICK SEQUENTIAL ORGAN FAILURE ASSESSMENT SCORE FOR PREDICTION OF SEPTIC SHOCK AFTER PERCUTANEOUS NEPHROLITHOTOMY: A SUB-ANALYSIS OF A MULTI-CENTER PROSPECTIVE TRIAL FROM THE EDGE CONSORTIUM

PD19-08 SYSTEMIC INFLAMMATORY RESPONSE SYNDROME AND QUICK SEQUENTIAL ORGAN FAILURE ASSESSMENT SCORE FOR PREDICTION OF SEPTIC SHOCK AFTER PERCUTANEOUS NEPHROLITHOTOMY: A SUB-ANALYSIS OF A MULTI-CENTER PROSPECTIVE TRIAL FROM THE EDGE CONSORTIUM

Role of hemogram-derived ratios in predicting intensive care unit admission in COVID-19 patients: a multicenter study

PurposeAs hyperinflammation is recognized as a driver of severe COVID-19 disease, checking markers of inflammation is gaining more attention. Our study aimed to evaluate the utility of cost-effective hemogram-derived ratios in predicting intensive care unit (ICU) admission in COVID-19 patients. MethodsThis multicenter retrospective study included hospitalized COVID-19 patients from four dedicated COVID-19 hospitals in Sylhet, Bangladesh. Data on demographics, clinical characteristics, laboratory parameters and survival outcomes were analyzed. Logistic regression analysis was used to identify the significance of each hemogram-derived ratio in predicting ICU admission. ResultsOf 442 included patients, 98 (22.17%) required ICU admission. At the time of admission, patients requiring ICU had a higher neutrophil count and lower lymphocyte and platelet counts than patients not requiring ICU. Peripheral capillary oxygen saturation at admission was significantly lower in those who subsequently required ICU admission. Neutrophil-to-lymphocyte ratio, derived neutrophil-to-lymphocyte ratio, neutrophil-to-platelet ratio, and systemic immune-inflammation index were significant predictors of ICU admission. ConclusionHemogram-derived ratios can be an effective tool in facilitating the early categorization of at-risk patients, enabling timely measures to be taken early in the disease course.

The predictive value of epicardial fat volume for clinical severity of COVID-19

IntroductionEpicardial adipose tissue serves as a source of inflammatory cytokines and mediators. Cytokine storm is an important cause of morbidity and mortality in coronavirus disease 2019 (COVID-19). ObjectivesTo investigate the association between epicardial fat volume (EFV), inflammatory biomarkers and clinical severity of COVID-19. MethodsThis retrospective study included 101 patients who were infected with COVID-19. Serum inflammatory biomarkers including C-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin (PCT) and ferritin levels were measured. Computed tomography images were analyzed and semi-automated measurements for EFV were obtained. The primary composite endpoint was admission to the intensive care unit (ICU) or death. ResultsThe primary composite endpoint occurred in 25.1% (n=26) of patients (mean age 64.8±14.8 years, 14 male). A total of 10 patients died. EFV, CRP, PCT, ferritin and IL-6 levels were significantly higher in ICU patients. Moreover, a positive correlation was determined between EFV and CRP (r: 0.494, p<0.001), PCT (r: 0.287, p=0.005), ferritin (r: 0.265, p=0.01) and IL-6 (r: 0.311, p=0.005). On receiver operating characteristic analysis, patients with EFV >102 cm3 were more likely to have severe complications. In multivariate logistic regression analysis, EFV independently predicted admission to the ICU at a significant level (OR: 1.02, 95% CI: 1.01-1.03, p=0.025). ConclusionEFV and serum CRP, IL-6, PCT and ferritin levels can effectively assess disease severity and predict the outcome in patients with COVID-19. EFV is an independent predictor of admission to the ICU in hospitalized COVID-19 patients.

Immunogenicity after second and third mRNA-1273 vaccination doses in patients receiving chemotherapy, immunotherapy, or both for solid tumours.

Immunogenicity after second and third mRNA-1273 vaccination doses in patients receiving chemotherapy, immunotherapy, or both for solid tumours.

App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden

The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to real-time COVID-19 surveillance. We enrolled 143,531 study participants (≥18 years) who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Here, we include data from 19,161 self-reported PCR tests to create a symptom-based model to estimate the individual probability of symptomatic COVID-19, with an AUC of 0.78 (95% CI 0.74–0.83) in an external dataset. These individual probabilities are employed to estimate daily regional COVID-19 prevalence, which are in turn used together with current hospital data to predict next week COVID-19 hospital admissions. We show that this hospital prediction model demonstrates a lower median absolute percentage error (MdAPE: 25.9%) across the five most populated regions in Sweden during the first pandemic wave than a model based on case notifications (MdAPE: 30.3%). During the second wave, the error rates are similar. When we apply the same model to an English dataset, not including local COVID-19 test data, we observe MdAPEs of 22.3% and 19.0% during the first and second pandemic waves, respectively, highlighting the transferability of the prediction model.