1
|
Pott H, LeBlanc JJ, ElSherif M, Hatchette TF, McNeil SA, Andrew MK. Predicting major clinical events among Canadian adults with laboratory-confirmed influenza infection using the influenza severity scale. Sci Rep 2024; 14:18378. [PMID: 39112632 PMCID: PMC11306731 DOI: 10.1038/s41598-024-67931-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 07/17/2024] [Indexed: 08/10/2024] Open
Abstract
We developed and validated the Influenza Severity Scale (ISS), a standardized risk assessment for influenza, to estimate and predict the probability of major clinical events in patients with laboratory-confirmed infection. Data from the Canadian Immunization Research Network's Serious Outcomes Surveillance Network (2011/2012-2018/2019 influenza seasons) enabled the selecting of all laboratory-confirmed influenza patients. A machine learning-based approach then identified variables, generated weighted scores, and evaluated model performance. This study included 12,954 patients with laboratory-confirmed influenza infections. The optimal scale encompassed ten variables: demographic (age and sex), health history (smoking status, chronic pulmonary disease, diabetes mellitus, and influenza vaccination status), clinical presentation (cough, sputum production, and shortness of breath), and function (need for regular support for activities of daily living). As a continuous variable, the scale had an AU-ROC of 0.73 (95% CI, 0.71-0.74). Aggregated scores classified participants into three risk categories: low (ISS < 30; 79.9% sensitivity, 51% specificity), moderate (ISS ≥ 30 but < 50; 54.5% sensitivity, 55.9% specificity), and high (ISS ≥ 50; 51.4% sensitivity, 80.5% specificity). ISS demonstrated a solid ability to identify patients with hospitalized laboratory-confirmed influenza at increased risk for Major Clinical Events, potentially impacting clinical practice and research.
Collapse
Affiliation(s)
- Henrique Pott
- Canadian Centre for Vaccinology, Dalhousie University, Halifax, Canada.
- Department of Medicine, Universidade Federal de São Carlos, Rod. Washington Luis, km 235, São Carlos, SP, 13656-905, Brazil.
| | - Jason J LeBlanc
- Canadian Centre for Vaccinology, Dalhousie University, Halifax, Canada
- Department of Pathology, Dalhousie University, Halifax, Canada
| | - May ElSherif
- Canadian Centre for Vaccinology, Dalhousie University, Halifax, Canada
| | - Todd F Hatchette
- Canadian Centre for Vaccinology, Dalhousie University, Halifax, Canada
- Department of Pathology, Dalhousie University, Halifax, Canada
| | - Shelly A McNeil
- Canadian Centre for Vaccinology, Dalhousie University, Halifax, Canada
- Department of Medicine (Infectious Diseases), Dalhousie University, Halifax, Canada
| | - Melissa K Andrew
- Canadian Centre for Vaccinology, Dalhousie University, Halifax, Canada
- Department of Medicine (Geriatrics), Dalhousie University, Halifax, Canada
| |
Collapse
|
2
|
Schober T, Morris SK, Bettinger JA, Burton C, Halperin SA, Jadavji T, Kazmi K, Modler J, Sadarangani M, Papenburg J. Antibiotic use in children hospitalised for influenza, 2010-2021: the Canadian Immunization Monitoring Program Active (IMPACT). Infection 2024; 52:865-875. [PMID: 37930625 DOI: 10.1007/s15010-023-02124-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 10/22/2023] [Indexed: 11/07/2023]
Abstract
PURPOSE To determine characteristics associated with inappropriate antibiotic use amongst children hospitalised for influenza. METHODS We performed active surveillance for laboratory-confirmed influenza hospitalizations amongst children ≤ 16 years old at the 12 Canadian Immunization Monitoring Program Active hospitals, from September 2010 to August 2021. Antibiotic use was presumed appropriate if any of the following indications were met: age < 1 month, immunocompromised, hemoglobinopathy, laboratory-confirmed bacterial infection, radiographically confirmed pneumonia, admission to an intensive care unit and mechanical ventilation. Regression analyses were used to identify baseline and clinical characteristics associated with antibiotic use amongst patients without an appropriate indication. RESULTS Amongst 8971 children, 6424 (71.6%) received any antibiotics during their hospitalisation. Amongst the 4429 children without an appropriate indication, 2366 (53.2%) received antibiotics. Antibiotic use amongst children without appropriate indication differed between study centres, ranging from 33.2% to 66.1% (interquartile range [IQR] 50.6-56.3%); it did not change significantly over time (p-value for trend = 0.28). In multivariable analyses, older age (adjusted odds ratio [aOR] 0.97, 95% confidence interval [CI] 0.96-0.99), presence of any high-risk condition (aOR 0.80, 95% CI 0.70-0.92), influenza virus type B (aOR 0.8, 95% CI 0.70-0.91) and croup (aOR 0.64, 95% CI 0.49-0.83) were associated with less, whilst fever ≥ 38.5 °C (aOR 1.82, 95% CI 1.42-2.35) and hospitalisation duration (aOR 1.12, 95% CI 1.09-1.15) were associated with more inappropriate antibiotic use. CONCLUSIONS Over two-third of children hospitalised for influenza received antibiotics, including over half of those without an appropriate indication for antibiotic treatment. Differences amongst study centres suggest the importance of contextual determinants of antibiotic use.
Collapse
Affiliation(s)
- Tilmann Schober
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Montreal Children's Hospital, McGill University Health Centre, E05.1905, 1001 Décarie Blvd, Montreal, QC, H4A 3J1, Canada
- Division of Pediatric Infectious Diseases, Dr. von Hauner Children's Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Shaun K Morris
- Division of Pediatric Infectious Diseases, Department of Pediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Julie A Bettinger
- Vaccine Evaluation Center, BC Children's Hospital Research Institute,, University of British Columbia, Vancouver, BC, Canada
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Catherine Burton
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Stollery Children's Hospital, University of Alberta, Edmonton, AB, Canada
| | - Scott A Halperin
- Canadian Center for Vaccinology, IWK Health Center, Dalhousie University, Halifax, NS, Canada
| | - Taj Jadavji
- Section of Infectious Diseases, Department of Pediatrics, Alberta Children's Hospital, University of Calgary, Calgary, AB, Canada
| | - Kescha Kazmi
- Division of Pediatric Infectious Diseases, Department of Pediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Jacqueline Modler
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montreal, QC, Canada
| | - Manish Sadarangani
- Vaccine Evaluation Center, BC Children's Hospital Research Institute,, University of British Columbia, Vancouver, BC, Canada
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Jesse Papenburg
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Montreal Children's Hospital, McGill University Health Centre, E05.1905, 1001 Décarie Blvd, Montreal, QC, H4A 3J1, Canada.
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montreal, QC, Canada.
- Division of Microbiology, Department of Clinical Laboratory Medicine, McGill University Health Centre, Montreal, QC, Canada.
| |
Collapse
|
3
|
Doyle JD, Garg S, O'Halloran AC, Grant L, Anderson EJ, Openo KP, Alden NB, Herlihy R, Meek J, Yousey‐Hindes K, Monroe ML, Kim S, Lynfield R, McMahon M, Muse A, Spina N, Irizarry L, Torres S, Bennett NM, Gaitan MA, Hill M, Cummings CN, Reed C, Schaffner W, Talbot HK, Self WH, Williams D. Performance of established disease severity scores in predicting severe outcomes among adults hospitalized with influenza-FluSurv-NET, 2017-2018. Influenza Other Respir Viruses 2023; 17:e13228. [PMID: 38111901 PMCID: PMC10725795 DOI: 10.1111/irv.13228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/10/2023] [Accepted: 11/11/2023] [Indexed: 12/20/2023] Open
Abstract
Background Influenza is a substantial cause of annual morbidity and mortality; however, correctly identifying those patients at increased risk for severe disease is often challenging. Several severity indices have been developed; however, these scores have not been validated for use in patients with influenza. We evaluated the discrimination of three clinical disease severity scores in predicting severe influenza-associated outcomes. Methods We used data from the Influenza Hospitalization Surveillance Network to assess outcomes of patients hospitalized with influenza in the United States during the 2017-2018 influenza season. We computed patient scores at admission for three widely used disease severity scores: CURB-65, Quick Sepsis-Related Organ Failure Assessment (qSOFA), and the Pneumonia Severity Index (PSI). We then grouped patients with severe outcomes into four severity tiers, ranging from ICU admission to death, and calculated receiver operating characteristic (ROC) curves for each severity index in predicting these tiers of severe outcomes. Results Among 8252 patients included in this study, we found that all tested severity scores had higher discrimination for more severe outcomes, including death, and poorer discrimination for less severe outcomes, such as ICU admission. We observed the highest discrimination for PSI against in-hospital mortality, at 0.78. Conclusions We observed low to moderate discrimination of all three scores in predicting severe outcomes among adults hospitalized with influenza. Given the substantial annual burden of influenza disease in the United States, identifying a prediction index for severe outcomes in adults requiring hospitalization with influenza would be beneficial for patient triage and clinical decision-making.
Collapse
Affiliation(s)
- Joshua D. Doyle
- Influenza Division, National Center for Immunization and Respiratory Diseases, CDCAtlantaGeorgiaUSA
- Epidemic Intelligence Service, CDCAtlantaGeorgiaUSA
| | - Shikha Garg
- Influenza Division, National Center for Immunization and Respiratory Diseases, CDCAtlantaGeorgiaUSA
| | - Alissa C. O'Halloran
- Influenza Division, National Center for Immunization and Respiratory Diseases, CDCAtlantaGeorgiaUSA
| | - Lauren Grant
- Influenza Division, National Center for Immunization and Respiratory Diseases, CDCAtlantaGeorgiaUSA
| | - Evan J. Anderson
- Emory University School of MedicineAtlantaGeorgiaUSA
- Atlanta Veterans Affairs Medical CenterAtlantaGeorgiaUSA
| | - Kyle P. Openo
- Emory University School of MedicineAtlantaGeorgiaUSA
- Atlanta Veterans Affairs Medical CenterAtlantaGeorgiaUSA
- Georgia Emerging Infections Program, Georgia Department of HealthAtlantaGeorgiaUSA
| | - Nisha B. Alden
- Colorado Department of Public Health and EnvironmentDenverColoradoUSA
| | - Rachel Herlihy
- Colorado Department of Public Health and EnvironmentDenverColoradoUSA
| | - James Meek
- Connecticut Emerging Infections ProgramYale School of Public HealthNew HavenConnecticutUSA
| | - Kimberly Yousey‐Hindes
- Connecticut Emerging Infections ProgramYale School of Public HealthNew HavenConnecticutUSA
| | | | - Sue Kim
- Communicable Disease Division, Michigan Department of Health and Human ServicesLansingMichiganUSA
| | - Ruth Lynfield
- Minnesota Department of HealthSaint PaulMinnesotaUSA
| | | | - Alison Muse
- New York State Department of HealthAlbanyNew YorkUSA
| | - Nancy Spina
- New York State Department of HealthAlbanyNew YorkUSA
| | | | - Salina Torres
- New Mexico Department of HealthAlbuquerqueNew MexicoUSA
| | - Nancy M. Bennett
- University of Rochester School of Medicine and DentistryRochesterNew YorkUSA
| | - Maria A. Gaitan
- University of Rochester School of Medicine and DentistryRochesterNew YorkUSA
| | - Mary Hill
- Salt Lake County Health DepartmentSalt Lake CityUtahUSA
| | - Charisse N. Cummings
- Influenza Division, National Center for Immunization and Respiratory Diseases, CDCAtlantaGeorgiaUSA
| | - Carrie Reed
- Influenza Division, National Center for Immunization and Respiratory Diseases, CDCAtlantaGeorgiaUSA
| | | | - H. Keipp Talbot
- Vanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Wesley H. Self
- Vanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Derek Williams
- Vanderbilt University School of MedicineNashvilleTennesseeUSA
| |
Collapse
|
4
|
Pajor MJ, Munigala S, Reynolds D, Zeigler J, Gebru D, Asaro PV, Lawrence SJ, Liang SY, Mudd PA. Predicting severe disease in patients diagnosed with seasonal influenza in the emergency department. J Am Coll Emerg Physicians Open 2023; 4:e13045. [PMID: 37745865 PMCID: PMC10511830 DOI: 10.1002/emp2.13045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 08/21/2023] [Accepted: 08/30/2023] [Indexed: 09/26/2023] Open
Abstract
Objectives We sought to develop an evidence-based tool to risk stratify patients diagnosed with seasonal influenza in the emergency department (ED). Methods We performed a single-center retrospective cohort study of all adult patients diagnosed with influenza in a large tertiary care ED between 2008 and 2018. We evaluated demographics, triage vital signs, chest x-ray and laboratory results obtained in the ED. We used univariate and multivariate statistics to examine the composite primary outcome of death or need for intubation. We validated our findings in patients diagnosed between 2018 and 2020. Results We collected data from 3128 subjects; 2196 in the derivation cohort and 932 in the validation cohort. Medical comorbidities, multifocal opacities or pleural effusion on chest radiography, older age, elevated respiratory rate, hypoxia, elevated blood urea nitrogen, blood glucose, blood lactate, and red blood cell distribution width were factors associated with intubation or death. We developed the Predicting Intubation in seasonal Influenza Patients diagnosed in the ED (PIIPED) risk-stratification tool from these factors. The PIIPED tool predicted intubation or death with an area under the receiver operating characteristic curve (AUC) of 0.899 in the derivation cohort and 0.895 in the validation cohort. A version of the tool including only factors available at ED triage, before laboratory or radiographic evaluation, exhibited AUC of 0.852 in the derivation cohort and 0.823 in the validation cohort. Conclusion Clinical findings during an ED visit predict severe outcomes in patients with seasonal influenza. The PIIPED risk stratification tool shows promise but requires prospective validation.
Collapse
Affiliation(s)
- Michael J. Pajor
- Department of Emergency MedicineWashington University School of MedicineSaint LouisMissouriUSA
| | - Satish Munigala
- Division of Infectious Diseases, Department of MedicineWashington University School of MedicineSaint LouisMissouriUSA
| | - Dan Reynolds
- Division of Pulmonary and Critical Care MedicineDepartment of MedicineWashington University School of MedicineSaint LouisMissouriUSA
| | - Julie Zeigler
- Department of Emergency MedicineWashington University School of MedicineSaint LouisMissouriUSA
| | - Danaye Gebru
- Department of Emergency MedicineWashington University School of MedicineSaint LouisMissouriUSA
| | - Phillip V. Asaro
- Department of Emergency MedicineWashington University School of MedicineSaint LouisMissouriUSA
| | - Steven J. Lawrence
- Division of Infectious Diseases, Department of MedicineWashington University School of MedicineSaint LouisMissouriUSA
| | - Stephen Y. Liang
- Department of Emergency MedicineWashington University School of MedicineSaint LouisMissouriUSA
- Division of Infectious Diseases, Department of MedicineWashington University School of MedicineSaint LouisMissouriUSA
| | - Philip A. Mudd
- Department of Emergency MedicineWashington University School of MedicineSaint LouisMissouriUSA
| |
Collapse
|
5
|
Leis AM, McSpadden E, Segaloff HE, Lauring AS, Cheng C, Petrie JG, Lamerato LE, Patel M, Flannery B, Ferdinands J, Karvonen‐Gutierrez CA, Monto A, Martin ET. K-medoids clustering of hospital admission characteristics to classify severity of influenza virus infection. Influenza Other Respir Viruses 2023; 17:e13120. [PMID: 36909298 PMCID: PMC9992770 DOI: 10.1111/irv.13120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 03/11/2023] Open
Abstract
Background Patients are admitted to the hospital for respiratory illness at different stages of their disease course. It is important to appropriately analyse this heterogeneity in surveillance data to accurately measure disease severity among those hospitalized. The purpose of this study was to determine if unique baseline clusters of influenza patients exist and to examine the association between cluster membership and in-hospital outcomes. Methods Patients hospitalized with influenza at two hospitals in Southeast Michigan during the 2017/2018 (n = 242) and 2018/2019 (n = 115) influenza seasons were included. Physiologic and laboratory variables were collected for the first 24 h of the hospital stay. K-medoids clustering was used to determine groups of individuals based on these values. Multivariable linear regression or Firth's logistic regression were used to examine the association between cluster membership and clinical outcomes. Results Three clusters were selected for 2017/2018, mainly differentiated by blood glucose level. After adjustment, those in C171 had 5.6 times the odds of mechanical ventilator use than those in C172 (95% CI: 1.49, 21.1) and a significantly longer mean hospital length of stay than those in both C172 (mean 1.5 days longer, 95% CI: 0.2, 2.7) and C173 (mean 1.4 days longer, 95% CI: 0.3, 2.5). Similar results were seen between the two clusters selected for 2018/2019. Conclusion In this study of hospitalized influenza patients, we show that distinct clusters with higher disease acuity can be identified and could be targeted for evaluations of vaccine and influenza antiviral effectiveness against disease attenuation. The association of higher disease acuity with glucose level merits evaluation.
Collapse
Affiliation(s)
- Aleda M. Leis
- Department of EpidemiologyUniversity of Michigan School of Public HealthAnn ArborMichiganUSA
| | - Erin McSpadden
- Department of EpidemiologyUniversity of Michigan School of Public HealthAnn ArborMichiganUSA
| | - Hannah E. Segaloff
- Department of EpidemiologyUniversity of Michigan School of Public HealthAnn ArborMichiganUSA
- Epidemic Intelligence ServiceCDCAtlantaGeorgiaUSA
- Wisconsin Department of Health ServicesMadisonWisconsinUSA
| | - Adam S. Lauring
- Departments of Internal Medicine and Microbiology and ImmunologyUniversity of MichiganAnn ArborMichiganUSA
| | - Caroline Cheng
- Department of EpidemiologyUniversity of Michigan School of Public HealthAnn ArborMichiganUSA
| | - Joshua G. Petrie
- Department of EpidemiologyUniversity of Michigan School of Public HealthAnn ArborMichiganUSA
- Marshfield Clinic Research InstituteMarshfieldWisconsinUSA
| | - Lois E. Lamerato
- Department of Public Health SciencesHenry Ford Health SystemDetroitMichiganUSA
| | - Manish Patel
- Influenza DivisionCenters for Disease Control and PreventionAtlantaGeorgiaUSA
| | - Brendan Flannery
- Influenza DivisionCenters for Disease Control and PreventionAtlantaGeorgiaUSA
| | - Jill Ferdinands
- Influenza DivisionCenters for Disease Control and PreventionAtlantaGeorgiaUSA
| | | | - Arnold Monto
- Department of EpidemiologyUniversity of Michigan School of Public HealthAnn ArborMichiganUSA
| | - Emily T. Martin
- Department of EpidemiologyUniversity of Michigan School of Public HealthAnn ArborMichiganUSA
| |
Collapse
|
6
|
Jiang Y, Lin YF, Shi S, Chen D, Shu Y. Effects of baloxavir and oseltamivir antiviral therapy on the transmission of seasonal influenza in China: A mathematical modeling analysis. J Med Virol 2022; 94:5425-5433. [PMID: 35770453 DOI: 10.1002/jmv.27969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/30/2022] [Accepted: 06/28/2022] [Indexed: 12/15/2022]
Abstract
New antiviral influenza treatments can effectively alleviate illness while reducing viral shedding. However, how such effects can translate into lower population infections of seasonal influenza in China remains unknown. To shed light on the public health impacts of novel antiviral agents for influenza, we constructed a dynamic transmission model to simulate the seasonal influenza epidemics in China. Two antivirus treatments, baloxavir and oseltamivir, were evaluated by estimating their impacts on the incidences of influenza infection in a single flu season. In the base-case analysis of a 10% antiviral treatment uptake rate, 2760 and 3420 per 10 000 persons contracted influenza under the treatment of baloxavir and oseltamivir, respectively. These incidence rates amounted to an 18.90% relative risk reduction (RRR) of infection associated with baloxavir in relation to oseltamivir. The corresponding RRR was 82.16% when the antiviral treatment uptake rate was increased to 35%. In addition, the peak of the prevalence of infected individuals per 10 000 persons under the baloxavir treatment was 177 (range: 93-274) fewer than that of oseltamivir. Our analyses suggest that the baloxavir treatment strategy reduces the incidence of influenza in China compared with oseltamivir in the setting of a seasonal flu epidemic. Also, increasing the uptake rate of antiviral treatment can potentially prevent millions of infections during a single flu season.
Collapse
Affiliation(s)
- Yawen Jiang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Yi-Fan Lin
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Si Shi
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Daqin Chen
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Yuelong Shu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China
| |
Collapse
|
7
|
Lan F, Fan B, Wang L, Xia L, Zhang T, Li W, Mao Y. The CURB65 score predicted 180-day mortality of non-small cell lung carcinoma patients with immune checkpoint inhibitor-associated pneumonitis: A pilot retrospective analysis. Front Oncol 2022; 12:927858. [PMID: 35978832 PMCID: PMC9376267 DOI: 10.3389/fonc.2022.927858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/04/2022] [Indexed: 11/21/2022] Open
Abstract
Introduction The immune checkpoint inhibitor-associated pneumonitis (CIP) is a particularly worrisome and potentially lethal form of immune-related adverse events. An objective and evidence-based assessment tool for evaluating the severity of CIP is in urgent need. CURB65 (consciousness, urea nitrogen, respiratory rate, blood pressure, and age) is a potential candidate to meet the need. Methods A retrospective study was conducted to explore preliminarily if CURB65 could predict the mortality in non-small cell lung carcinoma (NSCLC) patients with CIP. Results A total number of 28 NSCLC patients with CIP were included in the current study and classified into low-CURB65 group (n = 21) and high-CURB65 group (n = 7). Mortality after onset of CIP was consistently higher in the high-CURB65 group than in the low-CURB65 group (30-day: 57.1% vs. 0; 90-day: 71.4% vs. 4.76%; 180-day:71.4% vs. 14.29%). Two patients (9.5%) in the low-CURB65 group had severe CIP, and more than half of patients in the high-CURB65 group had severe CIP (p = 0.0008). The patients in the high-CURB65 group received more aggressive treatment. Both groups showed a predominant organizing pneumonia-like pattern on CT scan. CURB65 was moderately correlated with the American Society of Clinical Oncology (ASCO) grade of CIP, with a Pearson correlation coefficient R of 0.524. Conclusion CURB65 accurately stratified the risk of mortality in NSCLC patients with CIP. CURB65 might complement the ASCO grade in the assessment and prediction of mortality in these populations.
Collapse
Affiliation(s)
- Fen Lan
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Bo Fan
- Department of Respiratory and Critical Care Medicine, First People’s Hospital of Jiashan, Jiashan, China
| | - Lihua Wang
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Lixia Xia
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Ting Zhang
- Department of Radiotherapy, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Wen Li
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Yanxiong Mao, ; Wen Li,
| | - Yanxiong Mao
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Yanxiong Mao, ; Wen Li,
| |
Collapse
|