Predictive accuracy of 4C Mortality Score and Acute Physiology and Chronic Health Evaluation scores for mortality in COVID-19 patients admitted to intensive care unit

BACKGROUND AND OBJECTIVESTrauma is a leading cause of death worldwide and in Saudi Arabia. This study describes the injury profiles and ICU outcomes of patients in a tertiary trauma care referral center in Riyadh, Saudi Arabia.DESIGN AND SETTINGA retrospective analysis of ICU data collected prospectively over 5 years in a 21-bed medical and surgical intensive care unit (ICU) in a tertiary care teaching hospital.PATIENTS AND METHODSWe collected ICU data on all patients admitted secondary to motor vehicle accidents (MVAs), excluding patients younger than 18 years, brain dead patients and readmissions. We collected data on age, gender, and Glasgow coma scale score at admission, injury severity scores, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and other data. Multivariate logistic regression was used to identify predictors of mortality.RESULTSDuring the study period, of 1659 patients, MVA was the most common cause of injury (78.4%), followed by pedestrian accident (12.7%). ICU mortality included 221 patients (13.3%) during the study period. Severe head injury, age > 60 years, Glascow coma scale score, injury severity scores, APACHE II and international normalized ratio were independent predictors of mortality.CONCLUSIONMVA is very common in our country and leads to significant mortality and morbidity. Public education and strict law enforcement are needed to reduce these adverse events.

In-center hemodialysis (HD) patients face greater communicable disease risks, including drug-resistant bacterial colonization and viral hepatitis, compared with home dialysis patients, who limit these risks, avoiding three-times weekly travel to dialysis clinics for treatments (1,2). Minimizing the high coronavirus disease 2019 (COVID-19) morbidity in dialysis patients is essential (3). We explored severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive rates, COVID-19–related hospitalization, mortality, and intensive care unit (ICU) admission by dialysis modality in Ontario, Canada. Data collection was in accordance with Ontario Health's legislative authority under the Ontario Personal Health Information Protection Act of 2004. We linked seven administrative health databases including the Ontario Renal Reporting System, which captures the modality and treatment changes of all adult (>18 years) home dialysis (peritoneal dialysis or home HD) or center-based HD patients across Ontario. Our observation period was between March 1, 2020 and November 20, 2020. For individuals, demographic data, neighborhood income quintile, location, residence in long-term care, comorbidity, hospitalization, ICU admission, deaths, and SARS-CoV-2 provincial RT-PCR testing and results information was obtained. Assuming a 14-day SARS-CoV-2 incubation period, COVID-19 infection was ascribed to the dialysis modality after 14 consecutive days of a home or in-center modality. Patients were followed until death, kidney transplantation, or study end. Follow-up SARS-CoV-2 tests after initial positive results were excluded in ascertaining testing rates. Long-term care residents and regional programs with no COVID-19 infections during the study period were excluded. Event rates for the following were defined as: (1) SARS-CoV-2 tests; (2) positive SARS-CoV-2 tests; (3) initial COVID-19 hospitalization (hospitalization within 1 week of SARS-CoV-2 positivity and/or hospitalization with an international classification of diseases-10 diagnosis of COVID-19); (4) COVID-19 mortality or ICU admission (admission to an ICU with COVID-19 during the initial COVID-19 hospitalization or death within 30 days of COVID-19 admission or SARS-CoV-2 positivity); (5) non–COVID-19 mortality (deaths during the study period not occurring within 30 days of SARS-CoV-2 positivity or COVID-19 admission; and (6) all-cause mortality. Unadjusted and adjusted rate ratios (ARR) for these events were calculated by dialysis modality using a log-binomial model. Logistic regression was used to calculate the adjusted odds ratio (AOR) of hospitalization and ICU admission and/or 30-day mortality by dialysis modality among those with COVID-19 infection. All models were adjusted for factors as indicated in Table 1. Marginal generalized estimating equations and a working dependence correlation structure were used to account for patients contributing patient-times to both modalities. Analyses were performed using SAS Version 9.4 SAS Institute, Cary, NC). Table 1. - Adjusted rate ratios for SARS-CoV-2 and COVID-19 outcomes by dialysis modality Outcome Number with Outcome/ Person Days Rate (per 100,000 person-days) Unadjusted Rate Ratio(95% CI) Adjusted Rate Ratio(95% CI) SARS-CoV-2 tests In-center HD 20804/2,084,665 998 1.0 (Ref) 1.0 (Ref) Home dialysis 2317/761,059 304 0.35 (0.34 to 0.38) 0.37 (0.35 to 0.38) SARS-CoV-2 positive In-center HD 182/2,084,665 8.7 1.0 (Ref) 1.0 (Ref) Home dialysis 34/761,059 4.5 0.59 (0.41 to 0.86) 0.57 (0.39 to 0.83) COVID-19 Hospitalization In-center HD 177/2,084,665 8.5 1.0 (Ref) 1.0 (Ref) Home dialysis 29/761,059 3.8 0.53 (0.38 to 0.75) 0.57 (0.40 to 0.81) COVID-19 ICU admission 30-day mortality In-center HD 45/2,084,665 2.2 1.0 (Ref) 1.0 (Ref) Home dialysis 6/761,059 0.8 0.37 (0.16 to 0.85) 0.44 (0.18 to 1.09) Overall Mortality In-center HD 1030/2,084,665 49.4 1.0 (Ref) 1.0 (Ref) Home dialysis 283/761,059 37.2 0.75 (0.66 to 0.86) 0.94 (0.82 to 1.08) Non-COVID-19 mortality In-center HD 996/2,084,665 47.8 1.0 (Ref) 1.0 (Ref) Home dialysis 278/761,059 36.5 0.76 (0.67 to 0.87) 0.96 (0.83 to 1.09) Mortality reported over the study period. All models adjusted for age, gender, diabetes, length of time on dialysis, race, neighborhood income quintile, geographic location, and prior kidney transplantation. A total of 587 patients contributed to both modalities during the study period. Linked databases included: The Ontario Renal Reporting System, The Registered Persons Database, The Ontario Laboratory Information System COVID-19 database, The Ontario Renal Network COVID-19 data collection tool, The Canadian Institute for Health Information Discharge Abstract Database, The Ontario Health Insurance Plan (was used to determined residency in long-term care), and the Postal Code Conversion File (Statistics Canada; was linked via postal codes to determine neighborhood income quintiles and geographic location). 95% CI, 95% confidence interval; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; COVID-19, coronavirus disease; HD-hemodialysis; ICU, intensive care unit. We identified 3622 home dialysis (n=2853 peritoneal dialysis, n=769 home HD) and 9890 center-based HD patients (761,059 and 2,084,665 person-days, respectively). In-center patients were older (66.0±15 versus 63±15 years) and of longer dialysis vintage (median interquartile range, 2 [1–4] versus 2 [1–3] years), and a greater proportion had diabetes (53% versus 41%) compared with home dialysis patients. Fifty-three percent and 49% of home dialysis and in-center patients were in the greater Toronto area, respectively. SARS-CoV-2 testing rates were lower in home versus in-center dialysis patients (ARR, 0.37; 95% confidence interval [95% CI], 0.35 to 0.38). Six positive SARS-COV-2 episodes occurring within 14 days of dialysis initiation/modality transition were excluded. Positive SARS-CoV-2 tests and COVID-19 hospitalization rates were lower in home compared with in-center dialysis patients. COVID-19–related ICU admission and mortality (ARR, 0.44; 95% CI, 0.18 to 1.09) was lower in home versus in-center patients but did not reach statistical significance (Table 1). Among COVID-19–infected individuals, hospitalization occurred in 85% (29 of 34) and 86% (177 of 205) of home and in-center patients, respectively. Median length of hospitalization (interquartile range; days) was 13 (3–25) for in-center dialysis patients compared with 12 (11–27) for home dialysis patients. Mortality and/or ICU admission occurred in 18% (6 of 34) and 21% (45 of 205) of infected home and in-center patients, respectively. There were no differences in hospitalization (AOR, 1.3; 95% CI, 0.38 to 4.8) or death and/or ICU admission risks (AOR, 1.3; 95% CI, 0.37 to 4.8) in home compared with in-center COVID-19–infected patients. Non-COVID-19–related and overall mortality rates were similar in home versus center-based patients over the study period. We found a lower burden of COVID-19 infection, hospitalization, mortality, and ICU admission in community-dwelling home dialysis versus in-center patients. Our findings may relate to greater case finding in the in-center population, more frequent health care encounters, and routine screening/outbreak surveillance, which was at the program's discretion. If increased testing among in-center HD patients was the only explanation for the higher rates of COVID-19, one would have expected a disproportionate excess of milder cases (i.e., SARS-CoV-2 positivity not requiring hospital admission) among in-center compared with home dialysis patients. However, among in-center HD patients, we also observed higher rates of COVID-19 hospitalization, mortality, and ICU admission that may have been due to a higher infection rate rather than greater case-associated morbidity. Among COVID-19–infected individuals, we found no differences in the adjusted odds of hospitalization and ICU admission or 30-day mortality by home versus in-center treatment (albeit with limited power owing to low event rates). Our study captured over 90% of SARS-CoV-2 provincial tests. A limitation of this study is residual confounding based on unmeasured differences between in-center and home dialysis patients. One cannot exclude case-mix differences in the in-center versus home patients that may have accounted for the differences in COVID-19–related adverse event risks. We also could not distinguish between asymptomatic and symptomatic outpatient cases. Asymptomatic cases may have not been identified in the absence of mass screening. As community transmission of SARS-CoV-2 increased, and as HD facilities intensified infection prevention and control measures, differences in COVID-19 infection rates by dialysis modality may be attenuated compared with our observed findings over the early pandemic period. In the United States, rates of home dialysis continue to increase following the introduction of favorable reimbursement and policy reform (4,5). In addition to other purported benefits, a major shift to home-based dialysis care could render the ESKD population more resilient to the effects of COVID-19, reducing exposure episodes and total exposure time to SARS-CoV-2 while conferring lower future exposure risks to highly transmissible infections. Disclosures P.G. Blake is a contracted Medical Lead and Medical Director at Ontario Renal Network, Ontario Health, has received honoraria from Baxter Global for speaking engagements, and is on the Editorial Board of American Journal of Nephrology. J. Ip, Y. Tang, D. Thomas, and A. Yeung are salaried employees of Ontario Renal Network, Ontario Health. M. Oliver is a contracted Medical Lead at Ontario Renal Network, Ontario Health and is owner of Oliver Medical Management Inc., which licenses Dialysis Management Analysis and Reporting System software. He has received honoraria for speaking from Baxter Healthcare and participated on Advisory Boards for Janssen and Amgen. J. Perl reports grants from the Agency for Healthcare Research and Quality during the conduct of the study; personal fees from AstraZeneca Canada, Baxter Healthcare, DaVita Healthcare Partners, DCI, Fresenius Medical Care, LiberDi, Otsuka, and US Renal Care; research funding and salary support from Arbor Research Collaborative For Health and Agency for Healthcare Research and Quality; speakers bureau for Baxter Healthcare and Fresenius Medical Care; and is on the advisory board for Liberdi, outside of the submitted work. Funding None.

Outcome prediction by serum calprotectin in patients with COVID-19 in the emergency department

Patients who require management in the intensive care unit (ICU) for complications after allogeneic hematopoietic stem-cell transplantation (HSCT) generally have a poor outcome. We retrospectively studied whether the risk-prediction stratification systems commonly used for patients admitted to the ICU, that is, the Acute Physiology and Chronic Health Evaluation (APACHE) II and APACHE III systems, could be useful for identifying patients who should receive intensive care earlier. We reviewed the medical records of 210 patients who underwent allogeneic HSCT and found that 18 (8.6%) had been admitted to the ICU for acute respiratory failure (n=9), acute renal failure (n=7), and septic shock (n=2). The median APACHE II and III scores were, respectively, 16 (10-27) and 55 (22-87) at the onset of complications and 26 (15-43) and 101 (65-157) upon admission to the ICU. Thus, both the APACHE II and APACHE III scores at ICU admission were higher than those at the onset of complications (P <0.0001). Seventeen patients (94%) subsequently died, with a median ICU stay of 7.5 days (1-51 days), as a result of multiorgan failure (n=14), respiratory failure (n=2), and septic shock (n=1). The APACHE II and III scores of the sole surviving patient were, respectively, 21 and 71 at the onset and 24 and 86 upon transfer to the ICU. Thus, the APACHE scores in this study were lower than those reported for other surgical or medical patients treated in the ICU, despite their uniform poor prognosis. Although nine patients had developed grade III to IV acute graft-versus-host disease, which is the most common cause of morbidity and mortality after allogeneic HSCT, this was not fully evaluated in the current scoring systems. Application of these systems to HSCT will require adequate modification, with particular attention to organ dysfunction secondary to graft-versus-host disease.

Background and Aims:Outcome prediction of critically ill patients is an integral part of care in an Intensive Care Unit (ICU). Acute Physiology and Chronic Health Evaluation (APACHE) scoring systems provide an objective means of mortality prediction in ICU. The aim of this study was to compare the performance of APACHE II and IV scoring system in our ICU.Material and Methods:All patients admitted to the ICU between January and June 2014 and who met the inclusion criteria were evaluated. APACHE II and IV score were calculated during the first 24 h of ICU stay based on the worst values. All patients were followed up till discharge from the hospital or death. Statistical analysis was performed using SPSS version 19.0. Discrimination of the model for mortality was assessed using receiver operating characteristic curve and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test.Results:Of a total 1268, 1003 patients were included in this study. The mean (±standard deviation) admission APACHE II score was 19.4 ± 8.9, and APACHE IV score was 59.1 ± 27.2. The APACHE scores were significantly higher among nonsurvivors than survivors (P < 0.001). The overall crude hospital mortality rate was 17.6%. APACHE IV had better discriminative power area under the ROC curve ([AUC] –0.82) than APACHE II (AUC-0.75). Both APACHE II and APACHE IV had poor calibration.Conclusions:APACHE IV showed better discrimination compared to APACHE II in our ICU population. Both APACHE II and APACHE IV had poor calibration. However, APACHE II calibrated better compared to APACHE IV.

To analyze the influence of using mortality 1, 3, and 6 months after intensive care unit (ICU) admission instead of in-hospital mortality on the quality indicator standardized mortality ratio (SMR). A cohort study of 77,616 patients admitted to 44 Dutch mixed ICUs between 1 January 2008 and 1 July 2011. Four Acute Physiology and Chronic Health Evaluation (APACHE) IV models were customized to predict in-hospital mortality and mortality 1, 3, and 6 months after ICU admission. Models' performance, the SMR and associated SMR rank position of the ICUs were assessed by bootstrapping. The customized APACHE IV models can be used for prediction of in-hospital mortality as well as for mortality 1, 3, and 6 months after ICU admission. When SMR based on mortality 1, 3 or 6 months after ICU admission was used instead of in-hospital SMR, 23, 36, and 30% of the ICUs, respectively, received a significantly different SMR. The percentages of patients discharged from ICU to another medical facility outside the hospital or to home had a significant influence on the difference in SMR rank position if mortality 1 month after ICU admission was used instead of in-hospital mortality. The SMR and SMR rank position of ICUs were significantly influenced by the chosen endpoint of follow-up. Case-mix-adjusted in-hospital mortality is still influenced by discharge policies, therefore SMR based on mortality at a fixed time point after ICU admission should preferably be used as a quality indicator for benchmarking purposes.

Comparison of urine albumin within 6 hrs of intensive care unit (ICU) admission with demography, clinical classification, outcome, inotrope/vasopressor requirement, clinical assessment of mortality risk, and Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) II scores. Urine albumin-creatinine ratio (ACR) was measured on ICU admission (ACR 1) and after 4-6 hrs (ACR 2). A 17-bed general ICU in a university teaching hospital. Unselected medical (206) and surgical (225) patients recruited prospectively. None. Bedside urine ACR was measured by nurses using a Bayer DCA 2000 analyzer and expressed in mg/mmol (reference range <2.3). ACR 1 in medical and surgical patients was 15.5 (12.4-19.5) and 8.2 (5.9-11.1) mg/mmol, respectively (p = .0002), and ACR 2 was 9.0 (5.8-12.5) and 4.6 (3.6-5.3), respectively (p < .0001). For all patients, median (95% confidence interval) ACR fell from 11.2 (8.7-13.2) to 5.4 (4.7-6.8) mg/mmol 4-6 hrs after ICU admission (p < .0001). ACR 1 for nonsurvivors (n = 90) and survivors (n = 341) was 16.1 (11.2-21.3) and 8.8 (6.9-11.9), respectively (p = .0002) and ACR 2, 12.4 (8.2-18.9) and 4.8 (3.9-5.4), respectively (p < .0001). In both medical and surgical patients who died on the ICU, median ACR failed to decrease significantly following admission. ACR1 and ACR 2 were higher in patients who required inotropic or vasopressor support and correlated with duration of therapy. ACR 1 and 2 were inversely correlated with mean Po2/Fio2 ratio 48 hrs after ICU admission and positively correlated with duration of mechanical ventilation and ACR 1 with ICU stay. ACR 2 predicted mortality and ACR 1 inotrope requirement independent of clinical mortality risk assessment and APACHE II and SOFA scores. Urine albumin changes rapidly within the first 6 hrs following ICU admission and predicts ICU mortality and inotrope requirement as well as or better than APACHE II and SOFA scores. Serial urine albumin measurement may provide a means of monitoring the microvascular effects of systemic inflammation.

Does anticoagulation reduce mortality in patients with atrial fibrillation who later developed a COVID-19 infection?

Prognostic factors reliably predicting outcomes for critically ill adolescent and young adult (AYA) patients undergoing allogeneic hematopoietic cell transplantation (allo-HSCT) are lacking. We assessed transplant and intensive care unit (ICU)-related factors impacting patient outcomes. AYA patients who underwent allo-HSCT and required ICU admission at a Tertiary care Centre, during the period of 2003-2013, were included in this retrospective review. This was a non-interventional study. Only outcomes after the first allo-HSCT and index ICU admissions were analyzed. Disease-, transplant-, and ICU-related variables were analyzed to identify risk factors predictive of survival. Overall, 152 patients were included (males, 60.5%); median age at transplantation was 24 years (interquartile range [IQR] 18-32.5); median age at admission to the ICU was 25.8 years (IQR 19-34). Eighty-four percent underwent transplantation for a hematological malignancy; 129 (85%) received myeloablative conditioning. Seventy-one percent of ICU admissions occurred within the first year after allo-HSCT. ICU admission was primarily due to respiratory failure (47.3%) and sepsis (43.4%). One hundred and three patients (68%) died within 28 days of ICU admission. The 1- and 5-year overall survival rates were 19% and 17%, respectively. Main causes for ICU-related death were refractory septic shock with multiorgan failure (n = 49, 32%) and acute respiratory distress syndrome (ARDS) (n = 39, 26%). Univariate analysis showed that ICU mortality was associated with an Acute Physiology and Chronic Health Evaluation (APACHE) II score >20, a sequential organ failure assessment (SOFA score) > 12, a high lactate level, anemia, thrombocytopenia, leukopenia, hyperbilirubinemia, a high international normalized ratio (INR) and acute graft-versus-host disease (GVHD). Multivariate analysis identified thrombocytopenia, high INR, and acute GVHD as independent predictors of mortality. In AYA allo-HSCT patients admitted to the ICU, mortality remains high. Higher SOFA and APACHE scores, the need for organ support, thrombocytopenia, coagulopathy, and acute GVHD predict poor outcomes.

Both the Acute Physiology and Chronic Health Evaluation (APACHE) IV and Palliative Performance Index (PPI) are scales used to estimate intensive care unit (ICU) prognosis and mortality. To Compare the diagnostic utility of the PPI and APACHE IV and their subsequent implications in predicting ICU mortality at a community hospital. This was a Prospective Cohort Study. The study was conducted at the Community hospital ICU. Participants were 211 patients admitted from December 24, 2008 to June 11, 2009. An observer gathered appropriate data and performed the APACHE IV and PPI scales within 24 hours of admission. Results were then analyzed using standard formulae. The study included 211 participants in total with 211 participants in the PPI group (n = 211) and 162 in the APACHE IV group (n = 162). The APACHE score and PPI were found to be significant for predicting ICU mortality (P value of P < .002 and 99% CI of 13.74 to 20.32, P value of P < .001and 99% CI of 3.70 to 4.61, respectively). APACHE IV demonstrated a sensitivity of 84.6%, specificity of 96.0%, PPV of 64.7%, and NPV of 98.6%. In contrast, the PPI possessed a sensitivity of 69.2%, specificity of 96.0%, PPV of 64.7%, and NPV of 97.8%. Limitations may have occurred with the subjective nature of the PPI and Glasgow Coma Scale (GCS), along with meeting criterion for the APACHE IV. This prospective cohort study in the ICU of a community hospital demonstrated that both the APACHE IV and PPI were significant tools for predicting ICU mortality. When contrasting the 2 scales, the APACHE IV could more accurately rule in mortality when mortality occurred and rule out mortality when survival occurred.

Objective: To investigate the clinical characteristics and outcomes of obstetric patients requiring intensive care unit (ICU) admission in a tertiary hospital. Methods: We retrospectively analyzed the clinical data of all pregnant/postpartum patients admitted to a tertiary ICU from January 2014 to December 2018. Result: One hundred and thirty-three obstetric patients were analyzed. Most patients (114, 85.7%) were admitted postpartum, and 57.9% (n = 77) of ICU admissions were from obstetric causes. The most common obstetric cause of admission was obstetric hemorrhage (32, 24.1%), followed by pregnancy-associated hypertension (25, 18.8%). The most common non-obstetric cause of admission was cardiac disorder (16, 12%). Ninety-eight patients (73.7%) came from non-urban areas. We compared patients from non-urban areas versus urban areas: Acute Physiology and Chronic Health Evaluation (APACHE) II, 8.35 ± 3.14 versus 6.43 ± 2.59 (P = 0.002); standard prenatal care, 62.3% versus 90.3% (P = 0.004); transferred from another hospital, 25.5% versus 2.9% (P = 0.004); blood transfusion, 48% versus 22.9% (P = 0.010); plasmapheresis, 11.2% versus 0% (P = 0.039); multiple-organ dysfunction syndrome, 30.6% versus 11.4% (P = 0.026); mortality, 10.2% versus 2.9% (P = 0.176). Total maternal mortality in ICU was 8.3% (n = 11). The fetal mortality rate was 10.9% (n = 15). Conclusions: A multidisciplinary team approach is essential to improve the management of obstetric hemorrhage, hypertensive disorders and cardiac disorders, which may in turn significantly improve maternal outcomes. Health disparities existed between patients from non-urban versus urban areas: the former was sicker at admission, received less standard prenatal care, were more frequently transferred from other hospitals, received more interventions, developed more complication, and suffered a higher rate of maternal mortality.

PurposeDisease heterogeneity in coronavirus disease 2019 (COVID-19) may render the current one-size-fits-all treatment approach suboptimal. We aimed to identify and immunologically characterize clinical phenotypes among critically ill COVID-19 patients, and to assess heterogeneity of corticosteroid treatment effect.MethodsWe applied consensus k-means clustering on 21 clinical parameters obtained within 24 h after admission to the intensive care unit (ICU) from 13,279 COVID-19 patients admitted to 82 Dutch ICUs from February 2020 to February 2022. Derived phenotypes were reproduced in 6225 COVID-19 ICU patients from Spain (February 2020 to December 2021). Longitudinal immunological characterization was performed in three COVID-19 ICU cohorts from the Netherlands and Germany, and associations between corticosteroid treatment and survival were assessed across phenotypes.ResultsWe derived three phenotypes: COVIDICU1 (43% of patients) consisted of younger patients with the lowest Acute Physiology And Chronic Health Evaluation (APACHE) scores, highest body mass index (BMI), lowest PaO2/FiO2 ratio, and a 90-day in-hospital mortality rate of 18%. COVIDICU2 patients (37%) had the lowest BMI, were older and had higher APACHE scores and mortality rate (24%) than COVIDICU1. Patients with COVIDICU3 (20%) were the eldest with the most comorbidities, the highest APACHE scores, acute kidney injury and metabolic dysregulations, and the highest mortality rate (47%). These patients also displayed the most pronounced inflammatory response. Corticosteroid therapy started at day 5 [2–9] after ICU admission and administered for 5 [3–7] days was associated with an increased risk for 90-day mortality in patients with the COVIDICU1 and COVIDICU2 phenotypes (hazard ratio [HR] 1.59 [1.09–2.31], p = 0.015 and HR 1.79 [1.42–2.26], p < 0.001, respectively), but not in patients with the COVIDICU3 phenotype (HR 1.08 [0.76–1.54], p = 0.654).ConclusionOur multinational study identified three distinct clinical COVID-19 phenotypes, each exhibiting marked differences in demographic, clinical, and immunological features, and in the response to late and short-term corticosteroid treatment.

BackgroundTreatment and the clinical course during Emergency Department (ED) stay before Intensive Care Unit (ICU) admission may affect predicted mortality risk calculated by the Acute Physiology and Chronic Health Evaluation (APACHE)-IV, causing lead-time bias. As a result, comparing standardized mortality ratios (SMRs) among hospitals may be difficult if they differ in the location where initial stabilization takes place. The aim of this study was to assess to what extent predicted mortality risk would be affected if the APACHE-IV score was recalculated with the initial physiological variables from the ED. Secondly, to evaluate whether ED Length of Stay (LOS) was associated with a change (delta) in these APACHE-IV scores.MethodsAn observational multicenter cohort study including ICU patients admitted from the ED. Data from two Dutch quality registries were linked: the Netherlands Emergency department Evaluation Database (NEED) and the National Intensive Care Evaluation (NICE) registry. The ICU APACHE-IV, predicted mortality, and SMR based on data of the first 24 h of ICU admission were compared with an ED APACHE-IV model, using the most deviating physiological variables from the ED or ICU.ResultsA total of 1398 patients were included. The predicted mortality from the ICU APACHE-IV (median 0.10; IQR 0.03–0.30) was significantly lower compared to the ED APACHE-IV model (median 0.13; 0.04–0.36; p < 0.01). The SMR changed from 0.63 (95%CI 0.54–0.72) to 0.55 (95%CI 0.47–0.63) based on ED APACHE-IV. Predicted mortality risk changed more than 5% in 321 (23.2%) patients by using the ED APACHE-IV. ED LOS > 3.9 h was associated with a slight increase in delta APACHE-IV of 1.6 (95% CI 0.4–2.8) compared to ED LOS < 1.7 h.ConclusionPredicted mortality risks and SMRs calculated by the APACHE IV scores are not directly comparable in patients admitted from the ED if hospitals differ in their policy to stabilize patients in the ED before ICU admission. Future research should focus on developing models to adjust for these differences.

To determine whether scoring on the Acute Physiology and Chronic Health Evaluation (APACHE) III at admission can predict the development of multiple organ dysfunction syndrome and mortality in critically ill surgical patients. Prospective, inception-cohort study. Surgical intensive care unit of an urban, tertiary-care hospital. One hundred fourteen critically ill patients with surgically treated perforated gastrointestinal viscus. Calculation of APACHE II and APACHE III scores 24 hours after admission to the surgical intensive care unit and serial quantitation of organ dysfunction for the duration of critical care according to two different predefined scoring systems. Patients were stratified by survival, the development of organ dysfunction, and colon vs noncolonic perforation. Hospital mortality, length of stay in the surgical intensive care unit, and the development of organ dysfunction or overt organ failure. The mean (+/- SEM) APACHE II and APACHE III scores were 17.4 +/- 0.6 (range, 6 to 37) and 59.0 +/- 2.2 (range, 15 to 141), respectively. The incidence of organ dysfunction was 73% (64% in survivors). All severity scores were identical for colon perforation and noncolonic perforation subgroups. Nonsurvivors invariably had organ dysfunction. Overall length of stay in the intensive care unit was 12.0 +/- 1.6 days (8.7 +/- 1.2 days for survivors and 22.7 +/- 5.0 days for nonsurvivors). The APACHE scores and organ dysfunction or failure scores were significantly higher in nonsurvivors, and APACHE scores were higher in survivors with organ dysfunction than in those without it. Significant linear relationships were identified for APACHE II vs APACHE III scores (R2 = .66) and for all four combinations of APACHE scores and organ dysfunction or failure scores (R2 = .43 to .52). By multivariate analysis of variance, independent predictors of organ dysfunction or failure were APACHE III, increased age, and a prolonged stay in the surgical intensive care unit, but not the type of perforation. Neither APACHE II or APACHE III predicted mortality independently. The development of multiple organ dysfunction syndrome correlated with higher APACHE III scores but was independent of the type of perforation. Only the development of overt multiple organ failure predicted death. Combined use of APACHE III and the multiple organ dysfunction score provides improved prediction of multiple organ dysfunction syndrome, but further enhancements are needed before prediction of outcome in individual patients is reliable.

Long-Term Outcomes of Pulmonary Embolism Requiring Intensive Care Unit (ICU) Admission