scholarly journals The Age-AST-D Dimer (AAD) Regression Model Predicts Severe COVID-19 Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Fátima Higuera-de-la-Tijera ◽  
Alfredo Servín-Caamaño ◽  
Daniel Reyes-Herrera ◽  
Argelia Flores-López ◽  
Enrique J. A. Robiou-Vivero ◽  
...  
Keyword(s):  
Intensive Care ◽  
Liver Injury ◽  
Clinical Course ◽  
Severe Disease ◽  
Lactic Dehydrogenase ◽  
Icu Patients ◽  
Derivation Cohort ◽  
Icu Admission ◽  
Admission Time ◽  
D Dimer

Aim. Coronavirus disease (COVID-19) ranges from mild clinical phenotypes to life-threatening conditions like severe acute respiratory syndrome (SARS). It has been suggested that early liver injury in these patients could be a risk factor for poor outcome. We aimed to identify early biochemical predictive factors related to severe disease development with intensive care requirements in patients with COVID-19. Methods. Data from COVID-19 patients were collected at admission time to our hospital. Differential biochemical factors were identified between seriously ill patients requiring intensive care unit (ICU) admission (ICU patients) versus stable patients without the need for ICU admission (non-ICU patients). Multiple linear regression was applied, then a predictive model of severity called Age-AST-D dimer (AAD) was constructed ( n = 166 ) and validated ( n = 170 ). Results. Derivation cohort: from 166 patients included, there were 27 (16.3%) ICU patients that showed higher levels of liver injury markers ( P < 0.01 ) compared with non-ICU patients: alanine aminotrasnferase (ALT) 225.4 ± 341.2 vs. 41.3 ± 41.1 , aspartate aminotransferase (AST) 325.3 ± 382.4 vs. 52.8 ± 47.1 , lactic dehydrogenase (LDH) 764.6 ± 401.9 vs. 461.0 ± 185.6 , D-dimer (DD) 7765 ± 9109 vs. 1871 ± 4146 , and age 58.6 ± 12.7 vs. 49.1 ± 12.8 . With these finding, a model called Age-AST-DD (AAD), with a cut-point of <2.75 ( sensitivity = 0.797 and specificity = 0.391 , c − statistic = 0.74 ; 95%IC: 0.62-0.86, P < 0.001 ), to predict the risk of need admission to ICU ( OR = 5.8 ; 95% CI: 2.2-15.4, P = 0.001 ), was constructed. Validation cohort: in 170 different patients, the AAD   model < 2.75 ( c − statistic = 0.80 (95% CI: 0.70-0.91, P < 0.001 ) adequately predicted the risk ( OR = 8.8 , 95% CI: 3.4-22.6, P < 0.001 ) to be admitted in the ICU (27 patients, 15.95%). Conclusions. The elevation of AST (a possible marker of early liver injury) along with DD and age efficiently predict early (at admission time) probability of ICU admission during the clinical course of COVID-19. The AAD model can improve the comprehensive management of COVID-19 patients, and it could be useful as a triage tool to early classify patients with a high risk of developing a severe clinical course of the disease.

scholarly journals Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bongjin Lee ◽  
Kyunghoon Kim ◽  
Hyejin Hwang ◽  
You Sun Kim ◽  
Eun Hee Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Validation Cohort ◽  
External Validation ◽  
Learning Model ◽  
Derivation Cohort ◽  
Icu Admission ◽  
Early Stages ◽  
Machine Learning Model

AbstractThe aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

scholarly journals Lung ultrasound predicts clinical course but not outcome in COVID-19 ICU patients: a retrospective single-center analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Stephanie-Susanne Stecher ◽  
Sofia Anton ◽  
Alessia Fraccaroli ◽  
Jeremias Götschke ◽  
Hans Joachim Stemmler ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Lung Ultrasound ◽  
Clinical Course ◽  
Point Of Care ◽  
Clinical Deterioration ◽  
Normal Lung ◽  
Icu Patients ◽  
Standardized Protocol ◽  
Ultrasound Score

Abstract Background Point-of-care lung ultrasound (LU) is an established tool in the first assessment of patients with coronavirus disease (COVID-19). Purpose of this study was to evaluate the value of lung ultrasound in COVID-19 intensive care unit (ICU) patients in predicting clinical course and outcome. Methods We analyzed lung ultrasound score (LUS) of all COVID-19 patients admitted from March 2020 to December 2020 to the Internal Intensive Care Unit, Ludwig-Maximilians-University (LMU) of Munich. LU was performed according to a standardized protocol at ICU admission and in case of clinical deterioration with the need for intubation. A normal lung scores 0 points, the worst LUS has 24 points. Patients were stratified in a low (0–12 points) and a high (13–24 points) lung ultrasound score group. Results The study included 42 patients, 69% of them male. The most common comorbidities were hypertension (81%) and obesity (57%). The values of pH (7.42 ± 0.09 vs 7.35 ± 0.1; p = 0.047) and paO2 (107 [80–130] vs 80 [66–93] mmHg; p = 0.034) were significantly reduced in patients of the high LUS group. Furthermore, the duration of ventilation (12.5 [8.3–25] vs 36.5 [9.8–70] days; p = 0.029) was significantly prolonged in this group. Patchy subpleural thickening (n = 38; 90.5%) and subpleural consolidations (n = 23; 54.8%) were present in most patients. Pleural effusion was rare (n = 4; 9.5%). The median total LUS was 11.9 ± 3.9 points. In case of clinical deterioration with the need for intubation, LUS worsened significantly compared to baseline LU. Twelve patients died during the ICU stay (29%). There was no difference in survival in both LUS groups (75% vs 66.7%, p = 0.559). Conclusions LU can be a useful monitoring tool to predict clinical course but not outcome of COVID-19 ICU patients and can early recognize possible deteriorations.

scholarly journals Clinical diagnoses vs. autopsy findings in early deceased septic patients in the intensive care: a retrospective cohort study

2020 ◽  
Author(s):  
Rob G. H. Driessen ◽  
Bartholomeus G. H. Latten ◽  
Dennis C. J. J. Bergmans ◽  
Riquette P. M. G. Hulsewe ◽  
Johanna W. M. Holtkamp ◽  
...  
Keyword(s):  
Intensive Care ◽  
Clinical Diagnosis ◽  
Causes Of Death ◽  
Early Death ◽  
Class I ◽  
Icu Patients ◽  
Icu Admission ◽  
Clinical Diagnoses ◽  
Important Diagnostic Tool ◽  
Record Review

AbstractEarly death in sepsis occurs frequently; however, specific causes are largely unknown. An autopsy can contribute to ascertain causes of death. The objective of the study was to determine discrepancies in clinical diagnosis and postmortem findings in septic intensive care unit (ICU) patients deceased within 48 h after ICU admission. All septic ICU patients who deceased within 48 h after ICU admission were identified and included. Four intensivists determined the clinical cause of death by medical record review. An autopsy was performed within 24 h of death. Clinical diagnosis and postmortem findings were compared and classified as autopsy-identified missed clinical diagnoses and autopsy-refuted diagnoses. Class I and II missed major diagnoses using the Goldman criteria were scored. Between 2012 and 2017, 1107 septic patients were admitted to ICU. Of these, 344 patients (31%) died, of which 97 patients (28%) deceased within 48 h. In 32 (33%) early deceased patients, an autopsy was agreed. There were 26 autopsy-identified missed clinical diagnoses found, mostly myocardial infarction (n = 4) and pneumonia (n = 4). In four patients (13%), a class I discrepancy was found. In fourteen patients (42%), a class II discrepancy was found. In conclusion, an autopsy is an important diagnostic tool that can identify definite causes of death. These diagnoses deviate from diagnoses established during admission in early deceased sepsis patients.

Intensive Care Admissions and Associated Severity of Influenza B Versus A During Influenza B Vaccine–mismatched Seasons

2019 ◽  
Vol 69 (6) ◽  
pp. 1049-1052 ◽  
Author(s):  
Maya Korem ◽  
Efrat Orenbuch-Harroch ◽  
Eli Ben-Chetrit ◽  
Sarah Israel ◽  
Matan J Cohen ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Disease Severity ◽  
Influenza B ◽  
Icu Patients ◽  
Icu Admission ◽  
Severe Influenza ◽  
Trivalent Vaccine ◽  
Quadrivalent Vaccines

Abstract Patients admitted to hospital with influenza B and A in Jerusalem, Israel, during the 2015–2016 and 2017–2018 influenza seasons demonstrated similar rates of intensive care unit (ICU) admission and associated disease severity. Most (63%) influenza B ICU patients received influenza B–mismatched trivalent vaccine. These findings call into question the equivalence of trivalent and quadrivalent vaccines in preventing severe influenza B.

Factors Associated with Risk and Prognosis of Intensive Care Unit Admission in Patients with Acute Myeloid Leukemia: A Danish Nationwide Cohort Study

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 4783-4783
Author(s):  
Cecilie Velsoe Maeng ◽  
Christian Fynbo Christiansen ◽  
Kathleen Dori Liu ◽  
Lene Sofie Granfeldt Oestgaard
Keyword(s):  
Intensive Care ◽  
High Risk ◽  
Myeloid Leukemia ◽  
Risk Group ◽  
Remission Induction ◽  
First Year ◽  
Icu Patients ◽  
Icu Admission ◽  
Increased Risk ◽  
Acute Myeloid

Significance Patients with acute myeloid leukemia (AML) are at high risk of critical illness requiring admission to the intensive care unit (ICU) due to both disease- and treatment-related complications. A better understanding of risk factors for ICU admission and prognosis may help with prevention of life-threatening complications and informed decision-making when treating AML patients. Methods This study included all adult Danish AML patients, who received remission-induction chemotherapy alone or in combination with allogeneic stem cell transplantation from 2005 to 2016. The cohort was identified using the Danish Acute Leukemia Registry (DNLR) and information on ICU admission was obtained from the Danish Intensive Care Database. We examined risk of ICU admission within 1 and 3 years of diagnosis considering competing risk of death and investigated a number of possible risk factors for ICU admission. We computed 1-, 3-, and 5-year mortality from time of ICU admission and in the matched non-ICU comparison group using a risk set matching (1:1) on time since diagnosis, sex, and age. Finally, we used the pseudo-value approach to compute the relative risk (RR) of death in the ICU admitted cohort compared to the matched cohort. We adjusted for ECOG/WHO performance status (PS), year of diagnosis, cytogenetic risk group, number of comorbidities, and secondary/therapy-related AML. Results A total of 1383 AML patients were included in the study. The median follow-up time was 1.65 (IQR: 0.60-4.36) years. The risk of ICU admission within 1 year of AML diagnosis was 22.7%, and the risk within 3 years was 28.1%. Median time to ICU was 59 (IQR: 15-272) days. Male sex was associated with increased risk of ICU admission after 1 year (adjusted RR: 1.26, 95% CI: 1.01-1.57) and PS >1 was associated with an increased risk after both 1 year (adjusted RR: 1.74, 95% CI: 1.33-1.30) and 3 years (adjusted RR: 1.54, 95% CI: 1.22-1.96). Other factors listed in Table 1 (age, comorbidity, cytogenetic risk group, secondary or therapy-related AML, and year of diagnosis) were not associated with increased risk of ICU admission. In AML patients admitted to the ICU, the 1-year mortality from time of ICU admission was 69.2%, compared to a 1-year mortality rate of 31.0% in the matched non-ICU patients (adjusted RR: 3.25, 95% CI: 2.56-4.12). Long-term mortality was increased in ICU patients; 3-year mortality was 82.1% compared to 49.7% (adjusted RR: 2.43, 95% CI: 1.97-3.01), and the 5-year mortality was 83.1% compared to 60.6%, (adjusted RR: 2.12, 95% CI: 1.70-2.66). Conclusion In this national population-based cohort study, more than one fourth of AML patients treated with remission-induction chemotherapy were admitted to an ICU within 3 years of diagnosis with the majority of ICU admissions occurring within the first year. ICU admission was associated with high mortality, especially within the first year after admission. The risk of mortality decreased over time but remained increased 3 and 5 years after admission compared to the matched cohort. Early monitoring and management of high-risk patients may be effective in preventing ICU admissions and PS may serve as a possible tool to identify patients at high risk of ICU admission. Disclosures No relevant conflicts of interest to declare.

The Use Of Platelet Transfusions In The Intensive Care Unit and Impact On Platelet Count: A 30,000 Patient Registry Study

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1154-1154
Author(s):  
Shuoyan Ning ◽  
Rebecca Barty, MLT ◽  
Yang Liu ◽  
Nancy Heddle ◽  
Donald Arnold
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Platelet Count ◽  
Research Funding ◽  
Platelet Transfusion ◽  
Large Cohort ◽  
Clinical Effects ◽  
Icu Patients ◽  
Icu Admission ◽  
Platelet Transfusions

Abstract Background Thrombocytopenia is a common complication of critical illness and an independent risk factor for bleeding and death in the intensive care unit (ICU). Platelet transfusions are commonly used to improve platelet counts; however, the expected platelet increment from a transfusion in this setting has not been established. The objective of this study was to describe the frequency of platelet transfusion administration and their effect on platelet count increments in a large cohort of non-oncology critically ill adults. Methods We performed an analysis of a registry database, which was developed to capture clinical and laboratory data on all blood transfusions administered in 3 academic hospitals in Hamilton, Ontario, Canada. We included all patients ≥18 years who received one or more platelet transfusion during an ICU admission. Data validation was done by integrity checks with medical records and laboratory information system performed by a biostatistician. Non-transfused ICU patients were used as controls. The absolute increment in platelet count was calculated for each single platelet transfusion using the closest platelet count taken within 24 hours before the transfusion and 4-24 hours after the transfusion. Results Between April 2006 and October 2012, 33,222 patients were admitted to ICU, including 29,511 (88.8%) who did not have a diagnosis of cancer. Of those, 4,502 (15.3%) received one or more platelet transfusion during any ICU admission (n=4,690); 31.9% were female and median age at the time of first admission was 69 years (IQR 59-77). Among the 25,009 non-transfused patients admitted to ICU during the same period, 38.1% were female and the median age was 65 years (IQR 52–76). Median pre-transfusion platelet count was 87 x109/L (IQR 59-131) and a single platelet transfusion resulted in a median platelet count increment of 21 x109/L (IQR 6-40) as measured 6.7 hours (IQR 5.1-9.8) after the transfusion. There were 277 (25.4%) transfusions that yielded a platelet count increment of 5 x109/L or less. ICU mortality was 562/4,690 (12.4%) for patients who received a platelet transfusion, compared with 2,251/33,033(6.8%) for patients who were not transfused during their ICU stay. Summary/Conclusion Among this large cohort of non-oncology ICU patients, platelet transfusions were commonly administered for thrombocytopenia that was generally mild. In this setting one platelet transfusion resulted in a median platelet count rise of 21 x109/L. Many transfusion episodes yielded no appreciable increase in platelet count. Further studies are needed to determine the clinical effects of platelet transfusion in this setting controlling for confounding. Disclosures: Heddle: CIHR: Research Funding; Canadian Blood Services: Membership on an entity’s Board of Directors or advisory committees; Health Canada: Research Funding; Macopharma: Consultancy; ASH: Honoraria.

Risk Factors Associated With Resistance to Ciprofloxacin in Clinical Bacterial Isolates From Intensive Care Unit Patients

2007 ◽  
Vol 28 (3) ◽  
pp. 331-336 ◽  
Author(s):  
Phillip D. Levin ◽  
Robert A. Fowler ◽  
Cameron Guest ◽  
William J. Sibbald ◽  
Alex Kiss ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prior Treatment ◽  
Bacterial Isolates ◽  
Icu Patients ◽  
Gram Negative ◽  
Factors Associated ◽  
Icu Admission ◽  
Ciprofloxacin Resistance

Objective.To determine risk factors and outcomes associated with ciprofloxacin resistance in clinical bacterial isolates from intensive care unit (ICU) patients.Design.Prospective cohort study.Setting.Twenty-bed medical-surgical ICU in a Canadian tertiary care teaching hospital.Patients.All patients admitted to the ICU with a stay of at least 72 hours between January 1 and December 31, 2003.Methods.Prospective surveillance to determine patient comorbidities, use of medical devices, nosocomial infections, use of antimicrobials, and outcomes. Characteristics of patients with a ciprofloxacin-resistant gram-negative bacterial organism were compared with characteristics of patients without these pathogens.Results.Ciprofloxacin-resistant organisms were recovered from 20 (6%) of 338 ICU patients, representing 38 (21%) of 178 nonduplicate isolates of gram-negative bacilli. Forty-nine percent ofPseudomonas aeruginosaisolates and 29% ofEscherichia coliisolates were resistant to ciprofloxacin. In a multivariate analysis, independent risk factors associated with the recovery of a ciprofloxacin-resistant organism included duration of prior treatment with ciprofloxacin (relative risk [RR], 1.15 per day [95% confidence interval {CI}, 1.08-1.23];P< .001), duration of prior treatment with levofloxacin (RR, 1.39 per day [95% CI, 1.01-1.91];P= .04), and length of hospital stay prior to ICU admission (RR, 1.02 per day [95% CI, 1.01-1.03];P= .005). Neither ICU mortality (15% of patients with a ciprofloxacin-resistant isolate vs 23% of patients with a ciprofloxacin-susceptible isolate;P= .58 ) nor in-hospital mortality (30% vs 34%;P= .81 ) were statistically significantly associated with ciprofloxacin resistance.Conclusions.ICU patients are at risk of developing infections due to ciprofloxacin-resistant organisms. Variables associated with ciprofloxacin resistance include prior use of fluoroquinolones and duration of hospitalization prior to ICU admission. Recognition of these risk factors may influence antibiotic treatment decisions.

scholarly journals Staphylococcus aureusNasal Colonization and Subsequent Infection in Intensive Care Unit Patients: Does Methicillin Resistance Matter?

2010 ◽  
Vol 31 (6) ◽  
pp. 584-591 ◽  
Author(s):  
Hitoshi Honda ◽  
Melissa J. Krauss ◽  
Craig M. Coopersmith ◽  
Marin H. Kollef ◽  
Amy M. Richmond ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Confidence Interval ◽  
Methicillin Resistance ◽  
Hazard Ratio ◽  
Adjusted Hazard Ratio ◽  
Patient Specific ◽  
Icu Patients ◽  
Icu Admission

Background.Staphylococcus aureusis an important cause of infection in intensive care unit (ICU) patients. Colonization with methicillin-resistantS. aureus(MRSA) is a risk factor for subsequentS. aureusinfection. However, MRSA-colonized patients may have more comorbidities than methicillin-susceptibleS. aureus(MSSA)-colonized or noncolonized patients and therefore may be more susceptible to infection on that basis.Objective.To determine whether MRSA-colonized patients who are admitted to medical and surgical ICUs are more likely to develop anyS. aureusinfection in the ICU, compared with patients colonized with MSSA or not colonized withS. aureus,independent of predisposing patient risk factors.Design.Prospective cohort study.Setting.A 24-bed surgical ICU and a 19-bed medical ICU of a 1,252-bed, academic hospital.Patients.A total of 9,523 patients for whom nasal swab samples were cultured forS. aureusat ICU admission during the period from December 2002 through August 2007.Methods.Patients in the ICU for more than 48 hours were examined for an ICU-acquired S.aureusinfection, defined as development ofS. aureusinfection more than 48 hours after ICU admission.Results.S. aureuscolonization was present at admission for 1,433 (27.8%) of 5,161 patients (674 [47.0%] with MRSA and 759 [53.0%] with MSSA). An ICU-acquiredS. aureusinfection developed in 113 (2.19%) patients, of whom 75 (66.4%) had an infection due to MRSA. Risk factors associated with an ICU-acquiredS. aureusinfection included MRSA colonization at admission (adjusted hazard ratio, 4.70 [95% confidence interval, 3.07-7.21]) and MSSA colonization at admission (adjusted hazard ratio, 2.47 [95% confidence interval, 1.52-4.01]).Conclusion.ICU patients colonized with S.aureuswere at greater risk of developing aS. aureusinfection in the ICU. Even after adjusting for patient-specific risk factors, MRSA-colonized patients were more likely to developS. aureusinfection, compared with MSSA-colonized or noncolonized patients.

scholarly journals The Epidemiology of Vancomycin-Resistant Enterococcus Colonization in a Medical Intensive Care Unit

2003 ◽  
Vol 24 (4) ◽  
pp. 257-263 ◽  
Author(s):  
David K. Warren ◽  
Marin H. Kollef ◽  
Sondra M. Seiler ◽  
Scott K. Fridkin ◽  
Victoria J. Fraser
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Multiple Logistic Regression Analysis ◽  
Control Measures ◽  
Icu Patients ◽  
Icu Admission ◽  
Contact Precautions ◽  
Icu Stay ◽  
Vancomycin Resistant

AbstractObjective:To determine the epidemiology of colonization with vancomycin-resistant Enterococcus (VRE) among intensive care unit (ICU) patients.Design:Ten-month prospective cohort study.Setting:A 19-bed medical ICU of a 1,440-bed teaching hospital.Methods:Patients admitted to the ICU had rectal swab cultures for VRE on admission and weekly thereafter. VRE-positive patients were cared for using contact precautions. Clinical data, including microbiology reports, were collected prospectively during the ICU stay.Results:Of 519 patients who had admission stool cultures, 127 (25%) had cultures that were positive for VRE. Risk factors for VRE colonization identified by multiple logistic regression analysis were hospital stay greater than 3 days prior to ICU admission (adjusted odds ratio [AOR], 3.6; 95% confidence interval [CI95], 2.3 to 5.7), chronic dialysis (AOR, 2.4; CI95, 1.2 to 4.5), and having been admitted to the study hospital one to two times (AOR, 2.3; CI95,1.4 to 3.8) or more than two times (AOR, 6.5; CI95, 3.7 to 11.6) within the past 12 months. Of the 352 VRE-negative patients who had one or more follow-up cultures, 74 (21%) became VRE positive during their ICU stay (27 cases per 1,000 patient-ICU days).Conclusion:The prevalence of VRE culture positivity on ICU admission was high and a sizable fraction of ICU patients became VRE positive during their ICU stay despite contact precautions for VRE-positive patients. This was likely due in large part to prior VRE exposures in the rest of the hospital where these control measures were not being used.

Diagnostic Accuracy of Respiratory Distress Observation Scales as Surrogates of Dyspnea Self-report in Intensive Care Unit Patients

2015 ◽  
Vol 123 (4) ◽  
pp. 830-837 ◽  
Author(s):  
Romain Persichini ◽  
Frédérick Gay ◽  
Matthieu Schmidt ◽  
Julien Mayaux ◽  
Alexandre Demoule ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Heart Rate ◽  
Intensive Care ◽  
Facial Expression ◽  
Respiratory Distress ◽  
Validation Cohort ◽  
Self Report ◽  
Neck Muscle ◽  
Icu Patients ◽  
Derivation Cohort

Abstract Background: Dyspnea, like pain, can cause major suffering in intensive care unit (ICU) patients. Its evaluation relies on self-report; hence, the risk of being overlooked when verbal communication is impaired. Observation scales incorporating respiratory and behavioral signs (respiratory distress observation scales [RDOS]) can provide surrogates of dyspnea self-report in similar clinical contexts (palliative care). Methods: The authors prospectively studied (single center, 16-bed ICU, large university hospital) 220 communicating ICU patients (derivation cohort, 120 patients; separate validation cohort, 100 patients). Dyspnea was assessed by dyspnea visual analog scale (D-VAS) and RDOS calculated from its eight components (heart rate, respiratory rate, nonpurposeful movements, neck muscle use during inspiration, abdominal paradox, end-expiratory grunting, nasal flaring, and facial expression of fear). An iterative principal component analysis and partial least square regression process aimed at identifying an optimized D-VAS correlate (intensive care RDOS [IC-RDOS]). Results: In the derivation cohort, RDOS significantly correlated with D-VAS (r = 0.43; 95% CI, 0.29 to 0.58). A five-item IC-RDOS (heart rate, neck muscle use during inspiration, abdominal paradox, facial expression of fear, and supplemental oxygen) significantly better correlated with D-VAS (r = 0.61; 95% CI, 0.50 to 0.72). The median area under the receiver operating curve of IC-RDOS to predict D-VAS was 0.83 (interquartile range, 0.81 to 0.84). An IC-RDOS of 2.4 predicted D-VAS of 4 or greater with equal sensitivity and specificity (72%); an IC-RDOS of 6.3 predicted D-VAS of 4 or greater with 100% specificity. Similar results were found in the validation cohort. Conclusions: Combinations of observable signs correlate with dyspnea in communicating ICU patients. Future studies in noncommunicating patients will be needed to determine the responsiveness to therapeutic interventions and clinical usefulness.

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