94. Pneumonia Severity Scores Poorly Predict Severe Outcomes Among Adults Hospitalized with Influenza

Abstract Background Influenza can lead to severe outcomes among adults hospitalized with influenza, and causes substantial annual morbidity and mortality. We evaluated the performance of validated pneumonia severity indices in predicting severe influenza-associated outcomes. Methods We conducted a multicenter study within CDC’s Influenza Hospitalization Surveillance Network (FluSurv-NET) which included adults (≥ 18 years) hospitalized with laboratory-confirmed influenza during the 2017–18 influenza season. Medical charts were abstracted to obtain data on vital signs and laboratory values at admission on a stratified random sample of cases at a subset of hospitals at 11 network sites. Estimates were weighted to reflect the probability of selection. Cases were assigned to low- and high-risk groups based on the CURB-65 (‘Confusion, Urea, Respiratory rate, Blood pressure, Age ≥65’) index (high-risk cutoff = score ≥ 3), and the Pneumonia Severity Index (PSI) (high-risk cutoff = category V). We calculated area under receiver operating characteristic curves (AUROC), sensitivity, and specificity to estimate the performance of each index in predicting severe outcome categories: (1) intensive care unit (ICU) admission, 2) noninvasive mechanical ventilation (NIMV), (3) mechanical ventilation (MV), vasopressors, extracorporeal membrane oxygenation (ECMO) and (4) death. Results Among 27,523 adults hospitalized with influenza, 8665 (31%) were sampled for inclusion in this analysis; median age was 70 years and 92% had ≥ 1 chronic condition. A total of 1,366 (16%) were classified as high-risk by CURB-65 and 1,249 (14%) by PSI. Both indices had low discrimination for severe outcomes; the AUROC for CURB-65 ranged from 0.55 for ICU admission to 0.65 for death, and for PSI ranged from 0.58 for ICU admission to 0.73 for death. Risk status by CURB-65 was less sensitive than PSI in predicting MV, vasopressor, or ECMO usage as well as death (figure). The specificity of CURB-65 and PSI was similar against all outcomes (figure). Conclusion The CURB-65 and PSI indices performed poorly in predicting severe outcomes other than death; PSI had the best discrimination overall. Alternative approaches are needed to predict severe influenza-related outcomes and optimize clinical care. Disclosures All Authors: No reported Disclosures.

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Prognostic Prediction Value of qSOFA, SOFA, and Admission Lactate in Septic Patients with Community-Acquired Pneumonia in Emergency Department

Emergency Medicine International ◽

10.1155/2020/7979353 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Haijiang Zhou ◽

Tianfei Lan ◽

Shubin Guo

Keyword(s):

Emergency Department ◽

Mechanical Ventilation ◽

Mortality Rate ◽

Community Acquired Pneumonia ◽

Prediction Performance ◽

Secondary Outcome ◽

Icu Admission ◽

Severity Scores ◽

Prognostic Prediction ◽

Vasopressor Use

Background. Community-acquired pneumonia (CAP) is a leading cause of sepsis and common presentation to emergency department (ED) with a high mortality rate. The prognostic prediction value of sequential organ failure assessment (SOFA) and quick SOFA (qSOFA) scores in CAP in ED has not been validated in detail. The aim of this research is to investigate the prognostic prediction value of SOFA, qSOFA, and admission lactate compared with that of other commonly used severity scores (CURB65, CRB65, and PSI) in septic patients with CAP in ED. Methods. Adult septic patients with CAP admitted between Jan. 2017 and Jan. 2019 with increased admission SOFA ≥ 2 from baseline were enrolled. The primary outcome was 28-day mortality. The secondary outcome included intensive care unit (ICU) admission, mechanical ventilation, and vasopressor use. Prognostic prediction performance of the parameters above was compared using receiver operating characteristic (ROC) curves. Kaplan–Meier survival curves were compared using optimal cutoff values of qSOFA and admission lactate. Results. Among the 336 enrolled septic patients with CAP, 89 patients died and 247 patients survived after 28-day follow-up. The CURB65, CRB65, PSI, SOFA, qSOFA, and admission lactate levels were statistically significantly higher in the death group (P<0.001). qSOFA and SOFA were superior and the combination of qSOFA + lactate and SOFA + lactate outperformed other combinations of severity score and admission lactate in predicting both primary and secondary outcomes. Patients with admission qSOFA < 2 or lactate ≤ 2 mmol/L showed significantly prolonged survival than those patients with qSOFA ≥ 2 or lactate > 2 mmol/L (log-rank χ2 = 59.825, P<0.001). The prognostic prediction performance of the combination of qSOFA and admission lactate was comparable to the full version of SOFA (AUROC 0.833 vs. 0.795, Z = 1.378, P=0.168 in predicting 28-day mortality; AUROC 0.868 vs. 0.895, Z = 1.022, P=0.307 in predicting ICU admission; AUROC 0.868 vs. 0.845, Z = 0.921, P=0.357 in predicting mechanical ventilation; AUROC 0.875 vs. 0.821, Z = 2.12, P=0.034 in predicting vasopressor use). Conclusion. qSOFA and SOFA were superior to CURB65, CRB65, and PSI in predicting 28-day mortality, ICU admission, mechanical ventilation, and vasopressor use for septic patients with CAP in ED. Admission qSOFA with lactate is a convenient and useful predictor. Admission qSOFA ≥ 2 or lactate > 2 mmol/L would be very helpful in discriminating high-risk patients with a higher mortality rate.

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Predicting mortality with pneumonia severity scores: importance of model recalibration to local settings

Epidemiology and Infection ◽

10.1017/s0950268808000435 ◽

2008 ◽

Vol 136 (12) ◽

pp. 1628-1637 ◽

Cited By ~ 41

Author(s):

P. SCHUETZ ◽

M. KOLLER ◽

M. CHRIST-CRAIN ◽

E. STEYERBERG ◽

D. STOLZ ◽

...

Keyword(s):

Emergency Department ◽

Tertiary Care ◽

Pneumonia Severity Index ◽

Low Risk ◽

University Hospital ◽

Risk Scores ◽

Calibration Model ◽

Outpatient Management ◽

Pneumonia Severity ◽

Severity Scores

SUMMARYIn patients with community-acquired pneumonia (CAP) prediction rules based on individual predicted mortalities are frequently used to support decision-making for in-patient vs. outpatient management. We studied the accuracy and the need for recalibration of three risk prediction scores in a tertiary-care University hospital emergency-department setting in Switzerland. We pooled data from patients with CAP enrolled in two randomized controlled trials. We compared expected mortality from the original pneumonia severity index (PSI), CURB65 and CRB65 scores against observed mortality (calibration) and recalibrated the scores by fitting the intercept α and the calibration slope β from our calibration model. Each of the original models underestimated the observed 30-day mortality of 11%, in 371 patients admitted to the emergency department with CAP (8·4%, 5·5% and 5·0% for the PSI, CURB65 and CRB65 scores, respectively). In particular, we observed a relevant mortality within the low risk classes of the original models (2·6%, 5·3%, and 3·7% for PSI classes I–III, CURB65 classes 0–1, and CRB65 class 0, respectively). Recalibration of the original risk models corrected the miscalibration. After recalibration, however, only PSI class I was sensitive enough to identify patients with a low risk (i.e. <1%) for mortality suitable for outpatient management. In our tertiary-care setting with mostly referred in-patients, CAP risk scores substantially underestimated observed mortalities misclassifying patients with relevant risks of death suitable for outpatient management. Prior to the implementation of CAP risk scores in the clinical setting, the need for recalibration and the accuracy of low-risk re-classification should be studied in order to adhere with discharge guidelines and guarantee patients' safety.

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The Negative Impact of Obesity on the Occurrence and Prognosis of the 2019 Novel Coronavirus (COVID-19) Disease: A Systematic Review and Meta-Analysis

10.21203/rs.3.rs-86237/v1 ◽

2020 ◽

Author(s):

Tahereh Raeisi ◽

Hadis Mozaffari ◽

Nazaninzahra Sepehri ◽

Mohammad Alizadeh ◽

Mina Darand ◽

...

Keyword(s):

Intensive Care Unit ◽

Mechanical Ventilation ◽

Risk Factor ◽

Intensive Care ◽

Clinical Outcomes ◽

Meta Analysis ◽

Clinical Care ◽

Icu Admission ◽

Prevalence Of Obesity ◽

Novel Coronavirus

Abstract Background: the 2019 novel coronavirus (COVID-19) is an emerging pandemic, with a disease course varying from asymptomatic infection to critical disease resulting to death. Recognition of prognostic factors is essential because of its growing prevalence and high clinical costs. This meta-analysis aimed to evaluate the global prevalence of obesity in COVID-19 patients and to investigate whether obesity is a risk factor for the COVID-19, COVID-19 severity, and its poor clinical outcomes including hospitalization, intensive care unit (ICU) admission, need for mechanical ventilation, and mortality.Methods: The study protocol was registered on to PROSPERO (CRD42020203386). A systematic search of Scopus, Medline, and Web of Sciences was conducted on June 2020, to find pertinent studies. After selection, 54 studies from 10 different countries were included in the quantitative analyses. Pooled odds ratios (OR) with 95% confidence intervals (CIs) were calculated to assess the associations. Results: The prevalence of obesity was 33% (95% CI, 30.0%–35.0%) among patients with COVID-19. Obesity was significantly associated with susceptibility to COVID-19 (OR=2.42, 95% CI: 1.58 to 3.70; moderate certainty) and COVID-19 severity (OR=1.62, 95% CI: 1.48 to 1.76; low certainty). Furthermore, obesity was a significant risk factor for hospitalization (OR=1.75, 95% CI: 1.47 to 2.09; very low certainty), mechanical ventilation (OR=2.24, 95% CI: 1.70 to 2.94; low certainty), intensive care unit (ICU) admission (OR=1.75, 95% CI: 1.38 to 2.22; low certainty), and death (OR=1.23, 95% CI: 1.06 to 1.41; low certainty) in COVID-19 patients. In the subgroup analyses, these associations were supported by the majority of subgroups. Conclusions: Obesity is associated with COVID-19 and its poor clinical outcomes. Thus, it is highly recommended to consider obesity status in prognostic scores and improvement of guidelines for the clinical care of patients with COVID-19.

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One-Year Mortality Prediction in Elderly Patients With Suspected Pneumonia: The Usefulness of a Holistic Approach

10.21203/rs.3.rs-558655/v1 ◽

2021 ◽

Author(s):

Alberto Nascè ◽

Astrid Malézieux-Picard ◽

Landry Hakiza ◽

Thomas Fassier ◽

Dina Zekry ◽

...

Keyword(s):

Elderly Patients ◽

Rating Scale ◽

Nutritional Assessment ◽

Univariate Analysis ◽

Pneumonia Severity Index ◽

Mini Nutritional Assessment ◽

Functional Independence ◽

Pneumonia Severity ◽

Severity Scores

Abstract Background Pneumonia has an impact on long-term mortality in elderly patients. The risk factors associated with poor long-term outcomes are understated. The purpose of this study was to identify the predictors of 1-year mortality in older patients having a suspicion of pneumonia, using usual pneumonia severity scores and geriatric evaluation’s scores focused on comorbidities, nutritional status and functionality. Methods Consecutive patients over 65 years old and hospitalized with a suspicion of pneumonia were enrolled in a monocentric cohort from May 2015 to April 2016. Three scores were used to assess patients’ comorbidities (Cumulative Illness Rating Scale-Geriatric, CIRS-G), malnutrition (Mini Nutritional Assessment, MNA), functionality (Functional Independence Measure, FIM) respectively. Severity of pneumonia was assessed by using the Confusion, Urea, Respiratory Rate, Blood Pressure, and 65-years old score (CURB65), the Pneumonia Severity Index (PSI) and Sequential Organ Failure Assessment score (SOFA). With the exception of CIRS-G, all the scores were obtained prospectively within 48 hours after admission. The main outcome was 1-year mortality. Dates of death were obtained by consulting the cantonal register of deaths. Each score was analysed in univariate and multivariate models and logistic regressions were used to identify contributors to 1-year mortality. Results 200 patients were included (51 % male, mean age 83.8 +/- 7.7). The 1-year mortality rate was 30%. Scores associated with 1-year mortality were CURB-65 (p < .001), SOFA (p < .05), FIM (p < .01), CIRS-G (p < .001) and MNA (p < .001) in univariate analysis. Only CIRS-G (p < .05) and MNA (p < .05) were significant predictors of 1-year mortality in multivariate analysis. Conclusions Long-term prognosis of pneumonia was poor and we identified that scores assessing comorbidities and malnutrition were important predictors of 1-year mortality. This should be taken into account for evaluating elderly patients’ prognosis, levels and goals of care.

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Risk Categories in COVID-19 Based on Degrees of Inflammation: Data on More Than 17,000 Patients from the Spanish SEMI-COVID-19 Registry

Journal of Clinical Medicine ◽

10.3390/jcm10102214 ◽

2021 ◽

Vol 10 (10) ◽

pp. 2214

Author(s):

Manuel Rubio-Rivas ◽

Xavier Corbella ◽

Francesc Formiga ◽

Estela Menéndez Fernández ◽

María Martín Escalante ◽

...

Keyword(s):

Mechanical Ventilation ◽

High Risk ◽

Hospital Mortality ◽

Invasive Mechanical Ventilation ◽

High Flow ◽

Nasal Cannula ◽

High Flow Nasal Cannula ◽

Icu Admission ◽

Inflammatory Parameters ◽

Risk Categories

(1) Background: The inflammation or cytokine storm that accompanies COVID-19 marks the prognosis. This study aimed to identify three risk categories based on inflammatory parameters on admission. (2) Methods: Retrospective cohort study of patients diagnosed with COVID-19, collected and followed-up from 1 March to 31 July 2020, from the nationwide Spanish SEMI-COVID-19 Registry. The three categories of low, intermediate, and high risk were determined by taking into consideration the terciles of the total lymphocyte count and the values of C-reactive protein, lactate dehydrogenase, ferritin, and D-dimer taken at the time of admission. (3) Results: A total of 17,122 patients were included in the study. The high-risk group was older (57.9 vs. 64.2 vs. 70.4 years; p < 0.001) and predominantly male (37.5% vs. 46.9% vs. 60.1%; p < 0.001). They had a higher degree of dependence in daily tasks prior to admission (moderate-severe dependency in 10.8% vs. 14.1% vs. 17%; p < 0.001), arterial hypertension (36.9% vs. 45.2% vs. 52.8%; p < 0.001), dyslipidemia (28.4% vs. 37% vs. 40.6%; p < 0.001), diabetes mellitus (11.9% vs. 17.1% vs. 20.5%; p < 0.001), ischemic heart disease (3.7% vs. 6.5% vs. 8.4%; p < 0.001), heart failure (3.4% vs. 5.2% vs. 7.6%; p < 0.001), liver disease (1.1% vs. 3% vs. 3.9%; p = 0.002), chronic renal failure (2.3% vs. 3.6% vs. 6.7%; p < 0.001), cancer (6.5% vs. 7.2% vs. 11.1%; p < 0.001), and chronic obstructive pulmonary disease (5.7% vs. 5.4% vs. 7.1%; p < 0.001). They presented more frequently with fever, dyspnea, and vomiting. These patients more frequently required high flow nasal cannula (3.1% vs. 4.4% vs. 9.7%; p < 0.001), non-invasive mechanical ventilation (0.9% vs. 3% vs. 6.3%; p < 0.001), invasive mechanical ventilation (0.6% vs. 2.7% vs. 8.7%; p < 0.001), and ICU admission (0.9% vs. 3.6% vs. 10.6%; p < 0.001), and had a higher percentage of in-hospital mortality (2.3% vs. 6.2% vs. 23.9%; p < 0.001). The three risk categories proved to be an independent risk factor in multivariate analyses. (4) Conclusion: The present study identifies three risk categories for the requirement of high flow nasal cannula, mechanical ventilation, ICU admission, and in-hospital mortality based on lymphopenia and inflammatory parameters.

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Chest radiograph-based artificial intelligence predictive model for mortality in community-acquired pneumonia

BMJ Open Respiratory Research ◽

10.1136/bmjresp-2021-001045 ◽

2021 ◽

Vol 8 (1) ◽

pp. e001045

Author(s):

Jessica Quah ◽

Charlene Jin Yee Liew ◽

Lin Zou ◽

Xuan Han Koh ◽

Rayan Alsuwaigh ◽

...

Keyword(s):

Artificial Intelligence ◽

Chest Radiograph ◽

Risk Score ◽

Mortality Risk ◽

Characteristic Curve ◽

Pneumonia Severity Index ◽

Model Performance ◽

Community Acquired Pneumonia ◽

Pneumonia Severity ◽

Severity Scores

BackgroundChest radiograph (CXR) is a basic diagnostic test in community-acquired pneumonia (CAP) with prognostic value. We developed a CXR-based artificial intelligence (AI) model (CAP AI predictive Engine: CAPE) and prospectively evaluated its discrimination for 30-day mortality.MethodsDeep-learning model using convolutional neural network (CNN) was trained with a retrospective cohort of 2235 CXRs from 1966 unique adult patients admitted for CAP from 1 January 2019 to 31 December 2019. A single-centre prospective cohort between 11 May 2020 and 15 June 2020 was analysed for model performance. CAPE mortality risk score based on CNN analysis of the first CXR performed for CAP was used to determine the area under the receiver operating characteristic curve (AUC) for 30-day mortality.Results315 inpatient episodes for CAP occurred, with 30-day mortality of 19.4% (n=61/315). Non-survivors were older than survivors (mean (SD)age, 80.4 (10.3) vs 69.2 (18.7)); more likely to have dementia (n=27/61 vs n=58/254) and malignancies (n=16/61 vs n=18/254); demonstrate higher serum C reactive protein (mean (SD), 109 mg/L (98.6) vs 59.3 mg/L (69.7)) and serum procalcitonin (mean (SD), 11.3 (27.8) μg/L vs 1.4 (5.9) μg/L). The AUC for CAPE mortality risk score for 30-day mortality was 0.79 (95% CI 0.73 to 0.85, p<0.001); Pneumonia Severity Index (PSI) 0.80 (95% CI 0.74 to 0.86, p<0.001); Confusion of new onset, blood Urea nitrogen, Respiratory rate, Blood pressure, 65 (CURB-65) score 0.76 (95% CI 0.70 to 0.81, p<0.001), respectively. CAPE combined with CURB-65 model has an AUC of 0.83 (95% CI 0.77 to 0.88, p<0.001). The best performing model was CAPE incorporated with PSI, with an AUC of 0.84 (95% CI 0.79 to 0.89, p<0.001).ConclusionCXR-based CAPE mortality risk score was comparable to traditional pneumonia severity scores and improved its discrimination when combined.

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Pneumonia Severity Index in the Immunocompromised

Canadian Respiratory Journal ◽

10.1155/2006/195464 ◽

2006 ◽

Vol 13 (2) ◽

pp. 89-93 ◽

Cited By ~ 24

Author(s):

Kevin M Sanders ◽

Theodore K Marras ◽

Charles KN Chan

Keyword(s):

High Risk ◽

Solid Organ Transplantation ◽

Pneumonia Severity Index ◽

Severity Index ◽

Immunosuppressive Drugs ◽

Low Risk ◽

Solid Organ ◽

Concordance Index ◽

Pneumonia Severity ◽

Contemporary Sample

BACKGROUND: The pneumonia severity index (PSI) accounts for many comorbidities, but not immunosuppression.OBJECTIVES: To document the utility of the PSI to predict mortality in immunocompromised patients (IP) with community-acquired pneumonia (CAP).METHODS: Charts of 284 patients with immunosuppression and CAP were reviewed, and these patients were compared with a contemporary sample of non-IP with CAP. The ability of the PSI to predict mortality was assessed by using multiple logistic regression. Discrimination of the PSI was studied by using the concordance index.RESULTS: Thirty-nine of 284 IP died. Mortality varied according to the etiology of the immunosuppression. Patients with HIV, solid organ transplantation or treatment with immunosuppressive drugs (n=118) had a low in-hospital mortality (4.3%) and were classified as low risk. IP with hematological malignancies, chemotherapy, chest radiation or marrow transplantation (n=166) had a high mortality (20%) and were classified as high risk. Compared with non-IP, low-risk IP had similar PSI-controlled mortality (OR=0.9, P=0.80), whereas high-risk IP had significantly greater mortality (OR=2.8, P<0.0001). The concordance index revealed similar discrimination for the PSI in low-risk IP (0.77) and in non-IP (0.7), but inferior discrimination in high-risk patients (0.6).CONCLUSIONS: Patients with CAP and immunosuppression can be divided into low-risk and high-risk groups. The low-risk IP have mortality similar to non-IP and can be risk stratified by using the PSI.

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Comparative study of CURB-65, Pneumonia Severity Index and IDSA/ATS scoring systems in community acquired pneumonia in an Indian tertiary care setting

International Journal of Advances in Medicine ◽

10.18203/2349-3933.ijam20172088 ◽

2017 ◽

Vol 4 (3) ◽

pp. 693 ◽

Cited By ~ 1

Author(s):

S. Madhu ◽

Sabu Augustine ◽

Y. S. Ravi Kumar ◽

Kauser Kauser M. M. ◽

S. R. Vagesh Kumar ◽

...

Keyword(s):

Sensitivity And Specificity ◽

Tertiary Care ◽

Pneumonia Severity Index ◽

Scoring Systems ◽

Severity Index ◽

Community Acquired Pneumonia ◽

Class Iii ◽

Pneumonia Severity ◽

Icu Admission ◽

Class Iv

Background: Few comparative studies regarding prognostic scoring systems for community acquired pneumonia (CAP) are available from Indian context.Methods: Hospital-based prospective study to test the comparison between confusion, urea, respiratory rate, blood pressure, age over 65 years (CURB-65), Pneumonia severity index (PSI) and infectious diseases society of America/American thoracic society criteria (IDSA/ATS) scoring systems in patients with community acquired pneumonia.Results: CURB-65 class ≥III, PSI class ≥IV and patients who needed admission to intensive care unit (ICU) based on IDSA/ATS criteria were having sensitivity of 41.7%, 91.7% and 87.5% in predicting ICU admission with a specificity of 89.5%, 59.2% and 73.7% respectively. Their sensitivity in predicting death were 44.4%, 88.9% and 83.3% with a specificity of 87.8%, 54.9% and 68.3% respectively. In both PSI score and IDSA/ATS criteria risk scoring systems, mortality rate, need for ICU admission increased progressively with increasing scores but CURB-65 score did not show this correlation. The PSI class ≥IV was more sensitive in predicting ICU admission than CURB-65 and IDSA/ATS criteria.Conclusions: PSI was most sensitive in both predicting ICU admission and death whereas CURB-65 is most specific in predicting ICU admission and death. But CURB-65 is least sensitive in both predicting ICU admission and death. Even though IDSA/ATS criteria did not have highest sensitivity and specificity as single criteria it had modest sensitivity and specificity in predicting ICU admission and death.

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Early prediction of level-of-care requirements in patients with COVID-19

eLife ◽

10.7554/elife.60519 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 3

Author(s):

Boran Hao ◽

Shahabeddin Sotudian ◽

Taiyao Wang ◽

Tingting Xu ◽

Yang Hu ◽

...

Keyword(s):

Mechanical Ventilation ◽

Complex Disease ◽

Prediction Models ◽

Laboratory Data ◽

Vital Signs ◽

Signs And Symptoms ◽

Level Of Care ◽

Pneumonia Severity ◽

Severity Scores ◽

Care Requirements

This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.

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Utility of a Clinical Scoring System for Point of Care Triaging in COVID-19 Pneumonia

10.1101/2021.02.26.21252256 ◽

2021 ◽

Author(s):

Andrew J Gangemi ◽

Rohit Gupta ◽

Gustavo Fernandez-Romero ◽

Huaqing Zhao ◽

Maulin Patel ◽

...

Keyword(s):

Mechanical Ventilation ◽

Early Warning ◽

Early Warning System ◽

Invasive Mechanical Ventilation ◽

Warning System ◽

Resource Planning ◽

Hospital Charges ◽

Icu Admission ◽

Personnel Staffing ◽

Severity Scores

AbstractBackgroundSurges in COVID-19 disease cases can rapidly overwhelm healthcare resources; triaging to appropriate levels of care can assist in resource planning. At the beginning of the pandemic, we developed a simple triage tool, the Temple COVID-19 Pneumonia Triage Tool (TemCOV) based on a combination of clinical and radiographic features that are readily available on presentation to categorize and predict illness severity.MethodsWe prospectively examined 579 sequential cases admitted to Temple University Hospital who were assigned severity categories on admission. Our primary outcome was to compare the performance of TemCOV in predicting patients who have the highest likely of admission to the ICU at 24 and at 72 hours to other standard triage tools: the National Early Warning System (NEWS), the Modified Early Warning System (MEWS) and the CURB65 score. Additional endpoints included need for invasive mechanical ventilation (IMV) within 72 hours, total hospital admission charges, and mortality.Results26% of patients fell within our highest risk Category 4 and were more likely to require ICU admission at 24 hours (OR 11.51) and 72 hours (OR 8.6). Additionally they had the highest likelihood of needing IMV (OR 29.47) and in-hospital mortality (OR 2.37)., TemCOV performed similar to MEWS in predicting ICU admission at 24 hours (receive operator characteristic (ROC) curve area under the curve (AUC) 0.77 vs. 0.74, p=0.21) but better than NEWS2 and CURB65 (ROC AUC 0.77 vs. 0.69 and 0.77 vs. 0.64, respectively, p<0.01). While all severity scores had a weak correlation to hospital charges, the TemCOV performed the best among all severity scores measured (r=0.18); median hospital charges for Category 4 patients was $170,468 ($96,972-$487,556).ConclusionTemCOV is a simple triage score that can be used upon hospitalization in patients with COVID-19 that predicts the need for hospital resources such as ICU bed capacity, invasive mechanical ventilation and personnel staffing.

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