Literature DB >> 32735807

Risk factors for hospitalization, intensive care, and mortality among patients with asthma and COVID-19.

Liqin Wang1, Dinah Foer2, David W Bates3, Joshua A Boyce2, Li Zhou3.   

Abstract

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Year:  2020        PMID: 32735807      PMCID: PMC7387277          DOI: 10.1016/j.jaci.2020.07.018

Source DB:  PubMed          Journal:  J Allergy Clin Immunol        ISSN: 0091-6749            Impact factor:   10.793


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To the Editor: Respiratory viral illnesses are a well-established trigger of asthma exacerbations in children and adults and risk factor for poor outcomes and high health care utilization. Early studies from China identified chronic pulmonary disease as a risk factor for novel coronavirus disease 2019 (COVID-19) severity and death. US-based studies report that approximately 7% to 9% of hospitalized patients with COVID-19 had chronic lung disease, , with asthma more prevalent than chronic obstructive pulmonary disease (COPD) (9% vs 5.4%, respectively). Recent analyses of COVID-19 cohorts suggest that chronic respiratory disease may unexpectedly be less of a risk factor for COVID-19 infection and severity than nonrespiratory diseases. However, most studies to date do not distinguish asthma from COPD within chronic respiratory disease, limiting identification of asthma-specific risk factors. This case series used data (March 3, 2020, to June 8, 2020) from the Massachusetts-based Mass General Brigham (MGB, formerly Partners HealthCare) health system’s electronic health record. Inclusion criteria were (1) COVID-19 positive based on nasopharyngeal or sputum severe acute respiratory syndrome coronavirus 2 RT-PCR test administered between March 3, 2020, and May 20, 2020; (2) age 18 years or more at COVID-19 diagnosis; (3) previously diagnosed asthma, assessed as active asthma diagnosis on problem list or 2 or more separate encounters with International Classification of Diseases, Ninth Revision and/or International Classification of Diseases, Tenth Revision codes (detailed in Table E1 in this article’s Online Repository at www.jacionline.org) as a primary or secondary diagnosis; and (4) MGB primary care provider. Data on demographic characteristics, socioeconomic markers, baseline body mass index, insurance, smoking status, baseline outpatient-prescribed asthma medications, comorbidities including allergic and respiratory diseases, and clinical course of COVID-19 care were extracted. Patients’ encounter history was followed for 14 days from COVID-19 diagnosis for hospitalization and intensive care unit (ICU) admission, or by June 8, 2020, for mortality. We examined associations of demographic and clinical characteristics with hospitalization and ICU admission among those hospitalized for COVID-19, and mortality. Groups were compared using the Mann-Whitney-Wilcoxon test for continuous variables and the chi-square test or Fisher exact test for categorical variables. Unadjusted P value less than or equal to .1 was used as a cutoff for choosing variables to enter into subsequent risk factor analysis. We performed univariable and multivariable analysis using age-stratified logistic regression. Statistical significance was accepted at a 2-sided P value of less than or equal to .05. A Bonferroni-corrected P value of less than .0016 was used to adjust for multiple testing. Statistical analyses were performed in R software, version 3.5.3 (R Foundation for Statistical Computing). A total of 1827 patients met inclusion criteria (Table I ). The median age was 54 years (interquartile range, 37-66 years), and 1232 (67.4%) were female. More than two-thirds of patients were triaged to outpatient care; 565 patients (30.9%) were hospitalized, and of those, less than half (n = 236 [41.8%]) were admitted to the ICU. Almost all hospitalized patients were admitted to inpatient (99.3%) or ICU (97.9%) services within 14 days of COVID-19 diagnosis. The mortality rate among patients with asthma was 5.4% (n = 98) across all patients (outpatient and hospitalized), 15.6% for hospitalized patients, and 23.3% for ICU patients, with 70 (71.4%) patients dying within 14 days of COVID-19 diagnosis (see Table E2 in this article’s Online Repository at www.jacionline.org). Twenty-three (4.1%) hospitalized patients remained hospitalized at the time of study censoring. Mortality rate for all adult MGB COVID-19–positive patients during this same time period was 4.5% overall, 15.7% for hospitalized, and 23.5% for ICU patients.
Table I

Demographic and clinical characteristics of patients with a history of asthma and COVID-2019, by care setting and mortality

CharacteristicAll patients (n = 1827)Hospitalization (n = 1827)
ICU (n = 565)
Mortality (n = 1827)
Hospitalized (no) (n = 1262)Hospitalized (yes) (n = 565)P valueICU (no) (n = 329)ICU (yes) (n = 236)P valueDied (no) (n = 1729)Died (yes) (n = 98)P value
Demographic
Age (y), median (IQR)54 (37-66)50 (33-61)63 (50-75)<.00162 (49-75)65 (51.75-75).2853 (36-65)76 (68-85)<.001
 18-29252 of 1827 (13.8)231 of 1262 (18.3)21 of 565 (3.7)<.00113 of 329 (4)8 of 236 (3.4).71251 of 1729 (14.5)1 of 98 (1)<.001
 30-39260 of 1827 (14.2)208 of 1262 (16.5)52 of 565 (9.2)35 of 329 (10.6)17 of 236 (7.2)260 of 1729 (15)0 of 98 (0)
 40-49243 of 1827 (13.3)180 of 1262 (14.3)63 of 565 (11.2)37 of 329 (11.2)26 of 236 (11)242 of 1729 (14)1 of 98 (1)
 50-59377 of 1827 (20.6)273 of 1262 (21.6)104 of 565 (18.4)57 of 329 (17.3)47 of 236 (19.9)369 of 1729 (21.3)8 of 98 (8.2)
 60-69311 of 1827 (17)191 of 1262 (15.1)120 of 565 (21.2)67 of 329 (20.4)53 of 236 (22.5)292 of 1729 (16.9)19 of 98 (19.4)
 70-80208 of 1827 (11.4)101 of 1262 (8)107 of 565 (18.9)59 of 329 (17.9)48 of 236 (20.3)178 of 1729 (10.3)30 of 98 (30.6)
 ≥80176 of 1827 (9.6)78 of 1262 (6.2)98 of 565 (17.3)61 of 329 (18.5)37 of 236 (15.7)137 of 1729 (7.9)39 of 98 (39.8)
Sex: female1232 of 1827 (67.4)892 of 1262 (70.7)340 of 565 (60.2)<.001199 of 329 (60.5)141 of 236 (59.7).931177 of 1729 (68.1)55 of 98 (56.1).02
Race
 White1054 of 1827 (57.7)737 of 1262 (58.4)317 of 565 (56.1).02170 of 329 (51.7)147 of 236 (62.3).01980 of 1729 (56.7)74 of 98 (75.5).15
 Black297 of 1827 (16.3)189 of 1262 (15)108 of 565 (19.1)68 of 329 (20.7)40 of 236 (16.9)283 of 1729 (16.4)14 of 98 (14.3)
 Asian43 of 1827 (2.4)24 of 1262 (1.9)19 of 565 (3.4)8 of 329 (2.4)11 of 236 (4.7)43 of 1729 (2.5)0 of 98 (0)
 Other/unknown433 of 1827 (23.7)312 of 1262 (24.7)121 of 565 (21.4)83 of 329 (25.2)38 of 236 (16.1)423 of 1729 (24.5)10 of 98 (10.2)
Ethnicity, Hispanic494 of 1767 (28)359 of 1227 (29.3)135 of 540 (25).0878 of 310 (25.2)57 of 230 (24.8)>.99483 of 1672 (28.9)11 of 95 (11.6)<.001
Education level
 College and above540 of 1827 (29.6)391 of 1262 (31)149 of 565 (26.4).0380 of 329 (24.3)69 of 236 (29.2).42516 of 1729 (29.8)24 of 98 (24.5).55
 High school or equivalent620 of 1827 (33.9)416 of 1262 (33)204 of 565 (36.1)122 of 329 (37.1)82 of 236 (34.7)586 of 1729 (33.9)34 of 98 (34.7)
 Did not complete high school242 of 1827 (13.2)154 of 1262 (12.2)88 of 565 (15.6)47 of 329 (14.3)41 of 236 (17.4)227 of 1729 (13.1)15 of 98 (15.3)
 Unknown425 of 1827 (23.3)301 of 1262 (23.9)124 of 565 (21.9)80 of 329 (24.3)44 of 236 (18.6)400 of 1729 (23.1)25 of 98 (25.5)
Marital status
 Single764 of 1786 (42.8)566 of 1233 (45.9)198 of 553 (35.8)<.001130 of 319 (40.8)68 of 234 (29.1).03737 of 1691 (43.6)27 of 95 (28.4)<.001
 Married/partnered686 of 1786 (38.4)483 of 1233 (39.2)203 of 553 (36.7)111 of 319 (34.8)92 of 234 (39.3)661 of 1691 (39.1)25 of 95 (26.3)
 Divorced202 of 1786 (11.3)127 of 1233 (10.3)75 of 553 (13.6)40 of 319 (12.5)35 of 234 (15)190 of 1691 (11.2)12 of 95 (12.6)
 Widowed134 of 1786 (7.5)57 of 1233 (4.6)77 of 553 (13.9)38 of 319 (11.9)39 of 234 (16.7)103 of 1691 (6.1)31 of 95 (32.6)
Insurance type
 Commercial1065 of 1827 (58.3)799 of 1262 (63.3)266 of 565 (47.1)<.001152 of 329 (46.2)114 of 236 (48.3).11035 of 1729 (59.9)30 of 98 (30.6)<.001
 Medicare455 of 1827 (24.9)245 of 1262 (19.4)210 of 565 (37.2)119 of 329 (36.2)91 of 236 (38.6)392 of 1729 (22.7)63 of 98 (64.3)
 Medicaid269 of 1827 (14.7)187 of 1262 (14.8)82 of 565 (14.5)51 of 329 (15.5)31 of 236 (13.1)264 of 1729 (15.3)5 of 98 (5.1)
 Others38 of 1827 (2.1)31 of 1262 (2.5)7 of 565 (1.2)7 of 329 (2.1)0 of 236 (0)38 of 1729 (2.2)0 of 98 (0)
Smoking history
 Never smoker1109 of 1785 (62.1)817 of 1242 (65.8)292 of 543 (53.8)<.001164 of 310 (52.9)128 of 233 (54.9).581068 of 1690 (63.2)41 of 95 (43.2)<.001
 Current smoker136 of 1785 (7.6)91 of 1242 (7.3)45 of 543 (8.3)29 of 310 (9.4)16 of 233 (6.9)131 of 1690 (7.8)5 of 95 (5.3)
 Former smoker540 of 1785 (30.3)334 of 1242 (26.9)206 of 543 (37.9)117 of 310 (37.7)89 of 233 (38.2)491 of 1690 (29.1)49 of 95 (51.6)
BMI, median (IQR)30.23 (25.88-35.4)30.31 (25.98- 35.31)30.07 (25.82-35.59).5529.88 (25.14-35.12)30.07 (25.82-35.59).0330.38 (26.00-35.4)27.72 (23.00-34.31)>.99
 ≤24.9361 of 1812 (19.9)240 of 1252 (19.2)121 of 560 (21.6).4878 of 324 (24.1)43 of 236 (18.2).22327 of 1714 (19.1)34 of 98 (34.7)<.001
 25-29.9509 of 1812 (28.1)656 of 1252 (52.4)153 of 560 (27.3)88 of 324 (27.2)65 of 236 (27.5)483 of 1714 (28.2)26 of 98 (26.5)
 ≥30942 of 1812 (52)356 of 1252 (28.4)286 of 560 (51.1)158 of 324 (48.8)128 of 236 (54.2)904 of 1714 (52.7)38 of 98 (38.8)
Comorbidities§
Diabetes mellitus464 of 1827 (25.4)246 of 1262 (19.5)218 of 565 (38.6)<.001123 of 329 (37.4)95 of 236 (40.3).55416 of 1729 (24.1)48 of 98 (49)<.001
COPD292 of 1827 (16)129 of 1262 (10.2)163 of 565 (28.8)<.00186 of 329 (26.1)77 of 236 (32.6).11246 of 1729 (14.2)46 of 98 (47)<.001
Chronic kidney disease252 of 1827 (13.8)112 of 1262 (8.9)140 of 565 (24.8)<.00169 of 329 (21)71 of 236 (30.1).02206 of 1729 (11.9)46 of 98 (47)<.001
Chronic liver disease224 of 1827 (12.3)131 of 1262 (10.4)93 of 565 (16.5)<.00153 of 329 (16.1)40 of 236 (16.9).88211 of 1729 (12.2)13 of 98 (13.3).88
Cardiovascular disease589 of 1827 (32.2)309 of 1262 (24.5)280 of 565 (49.6)<.001153 of 329 (46.5)127 of 236 (53.8).1515 of 1729 (29.8)74 of 98 (75.5)<.001
Hypertension837 of 1827 (45.8)465 of 1262 (36.8)372 of 565 (65.8)<.001209 of 329 (63.5)163 of 236 (69.1).2758 of 1729 (43.8)79 of 98 (80.6)<.001
Allergic rhinitis518 of 1827 (28.4)390 of 1262 (30.9)128 of 565 (22.7)<.00172 of 329 (21.9)56 of 236 (23.7).68500 of 1729 (28.9)18 of 98 (18.4).03
Chronic rhinosinusitis95 of 1827 (5.2)67 of 1262 (5.3)28 of 565 (5).8418 of 329 (5.5)10 of 236 (4.2).5690 of 1729 (5.2)5 of 98 (5.1)>.99
Atopic dermatitis51 of 1827 (2.8)33 of 1262 (2.6)18 of 565 (3.2).67 of 329 (2.1)11 of 236 (4.7).1448 of 1729 (2.8)3 of 98 (3.1).75
Controller medications
ICS310 of 1827 (17)227 of 1262 (18)83 of 565 (14.7).09554 of 329 (16.4)29 of 236 (12.3).21297 of 1729 (17.2)13 of 98 (13.3).39
ICS-LABA combination289 of 1827 (15.8)185 of 1262 (14.7)104 of 565 (18.4).0563 of 329 (19.1)41 of 236 (17.4).67274 of 1729 (15.8)15 of 98 (15.3)>.99
Anticholinergic73 of 1827 (4)40 of 1262 (3.2)33 of 565 (5.8).0120 of 329 (6.1)13 of 236 (5.5).9266 of 1729 (3.8)7 of 98 (7.1).11
Biologic16 of 1827 (.9)11 of 1262 (.9)5 of 565 (.9).492 of 329 (.61)3 of 236 (1.3).6515 of 1729 (.9)1 of 98 (1).59
Leukotriene modifier134 of 1827 (7.3)86 of 1262 (6.8)48 of 565 (8.5).2429 of 329 (8.8)19 of 236 (8.1).87127 of 1729 (7.3)7 of 98 (7.1)>.99
Reliever medications
SABA
 SABA-only392 of 1827 (21.5)302 of 1262 (23.9)90 of 565 (15.9)<.00142 of 329 (12.8)48 of 236 (20.3).02380 of 1729 (22)12 of 98 (12.2).045
 With controller450 of 1827 (24.6)316 of 1262 (25)134 of 565 (23.7)87 of 329 (26.4)47 of 236 (19.9)427 of 1729 (24.7)23 of 98 (23.5)
 None985 of 1827 (53.9)644 of 1262 (51)341 of 565 (60.4)200 of 329 (60.8)141 of 236 (59.7)922 of 1729 (53.3)63 of 98 (64.3)
SABA-anticholinergic combination106 of 1827 (5.8)48 of 1262 (3.8)58 of 565 (10.3)<.00132 of 329 (9.7)26 of 236 (11).7292 of 1729 (5.3)14 of 98 (14.3)<.001
Death#98 of 1827 (5.4)10 of 1262 (.8)88 of 565 (15.6)<.00133 of 329 (10)55 of 236 (23.3)<.0010 of 98 (0)98 of 98 (100)NA

BMI, Body mass index; FDA, Food and Drug Administration; ICD, International Classification of Diseases; IQR, interquartile range; LABA, long-acting beta-agonist.

Data reflect patients diagnosed with COVID-19 between March 3, 2020, and May 20, 2020. Of 78,870 patients tested for COVID-19 in this period, 60.2% (n = 47,468) were female. Characteristics (except death) as of date of COVID-19 diagnosis.

All P values are unadjusted.

Self-reported.

As recorded by ICD code or problem list in the electronic health record. Diabetes mellitus includes type 1 and type 2. Chronic rhinosinusitis includes with and without nasal polyps.

Active prescription initiated within the 12 months before COVID-19 diagnosis.

With an FDA-approved indication for asthma.

Mortality data collected until June 8, 2020.

Demographic and clinical characteristics of patients with a history of asthma and COVID-2019, by care setting and mortality BMI, Body mass index; FDA, Food and Drug Administration; ICD, International Classification of Diseases; IQR, interquartile range; LABA, long-acting beta-agonist. Data reflect patients diagnosed with COVID-19 between March 3, 2020, and May 20, 2020. Of 78,870 patients tested for COVID-19 in this period, 60.2% (n = 47,468) were female. Characteristics (except death) as of date of COVID-19 diagnosis. All P values are unadjusted. Self-reported. As recorded by ICD code or problem list in the electronic health record. Diabetes mellitus includes type 1 and type 2. Chronic rhinosinusitis includes with and without nasal polyps. Active prescription initiated within the 12 months before COVID-19 diagnosis. With an FDA-approved indication for asthma. Mortality data collected until June 8, 2020. Compared with the outpatient group, hospitalized patients had higher baseline use of inhaled-corticosteroid (ICS)-long-acting-beta-agonist combination and anticholinergic controller medications. Controller medication use did not differ in the hospitalized general inpatient versus ICU groups. More patients in the outpatient group had only a short-acting beta-agonist (SABA) prescribed in the previous year compared with hospitalized patients (P < .001), whereas a higher percentage of hospitalized patients had been prescribed combined SABA-anticholinergic reliever medications (P < .001) (Table I); 54.7% of patients prescribed SABA-anticholinergic relievers were also prescribed a controller medication. Only baseline SABA medications differed between general inpatient and ICU patients (P = .024). Patients receiving biologics for asthma therapy did not differ across groups (see Table E3 in this article’s Online Repository at www.jacionline.org). Increased risk for hospitalization versus outpatient care was significantly associated (Table II ) with older age (unadjusted odds ratio [OR], 1.46; 95% CI, 1.38-1.55; P < .001, for every increase of 10 years), male sex (adjusted OR [aOR], 1.75; 95% CI, 1.36-2.24; P < .001), black (aOR, 1.65; 95% CI, 1.19-2.27; P = .002) and Asian (aOR, 3.19; 95% CI, 1.56-6.54; P = .0015) race, diabetes mellitus (aOR, 1.33; 95% CI, 1.0-1.75; P < .05), comorbid COPD (aOR, 1.92; 95% CI, 1.35-2.72; P < .001), cardiovascular disease (aOR, 1.52; 95% CI, 1.16-2.0; P = .002), or an active outpatient prescription for combined SABA-anticholinergic medication (aOR, 1.74; 95% CI, 1.09-2.8; P < .05). Sixty-two percent of hospitalized SABA-anticholinergic users also had COPD. Patients with only SABA prescriptions were less likely to be hospitalized (aOR, .59; 95% CI, 0.43-0.8; P < .001). Male sex, Asian race, COPD, and SABA-only remained significant after correcting for multiple comparisons (bolded aORs in Table II).
Table II

Risk factors associated with hospitalization, intensive care, and mortality among patients with a history of asthma and COVID-2019

VariableHospitalization
ICU
Mortality
Univariable analysis (n = 1827)Multivariable analysis (n = 1717)Univariable analysis (n = 565)Multivariable analysis (n = 543)Univariable analysis (n = 1827)Multivariable analysis (n = 1678)
Age1.46 (1.38-1.55)NA1.03 (0.94-1.13)NA2.25 (1.95-2.63)NA
Sex: male1.64 (1.32-2.05)1.75 (1.36-2.24)§1.04 (0.73-1.46)NA1.7 (1.1-2.62)1.95 (1.16-3.26)
Race
 White1.01.01.01.01.0NA
 Black1.69 (1.26-2.26)1.65 (1.19-2.27)0.67 (0.43-1.06)0.68 (0.42-1.1)0.94 (0.51-1.75)NA
 Asian3.01 (1.55-5.85)3.19 (1.56-6.54)1.5 (0.58-3.85)2.16 (0.79-5.92)NANA
 Other/unknown1.28 (0.98-1.67).93 (0.61-1.42)0.51 (0.32-0.81)0.6 (0.37-0.99)0.61 (0.3-1.23)NA
Ethnicity, Hispanic1.11 (0.87-1.42)1.34 (0.9-1.98)0.98 (0.66-1.47)NA0.6 (0.31-1.17)0.83 (0.41-1.71)
Marital status
 Single1.01.01.01.01.01.0
 Married/partnered0.78 (0.6-1)0.94 (0.72-1.25)1.59 (1.05-2.39)1.56 (1.01-2.41)0.58 (0.32-1.04)0.6 (0.32-1.11)
 Divorced0.89 (0.63-1.27)0.92 (0.63-1.36)1.69 (0.97-2.96)1.7 (0.95-3.03)0.69 (0.33-1.43)0.69 (0.31-1.51)
 Widowed1.31 (0.85-2)1.41 (0.88-2.28)2.32 (1.27-4.24)2.17 (1.15-4.09)1.53 (0.83-2.82)1.85 (0.93-3.71)
Education level
 College and above1.01.01.0NA1.0NA
 High school or equivalent1.37 (1.05-1.78)1.13 (0.84-1.53)0.77 (0.5-1.18)NA1.16 (0.66-2.03)NA
 Did not complete high school1.54 (1.1-2.17)1.17 (0.78-1.75)0.99 (0.59-1.69)NA1.33 (0.66-2.68)NA
 Unknown1.02 (0.76-1.37)0.84 (0.6-1.18)0.63 (0.39-1.03)NA1.12 (0.61-2.04)NA
Insurance type
 Commercial1.01.01.01.01.01.0
 Medicaid1.64 (1.21-2.24)1.21 (0.84-1.74)0.85 (0.51-1.42)1.05 (0.61-1.84)NA0.97 (0.31-3.04)
 Medicare1.18 (0.89-1.56)0.93 (0.68-1.27)1.04 (0.69-1.57)1.04 (0.67-1.62)1.56 (0.94-2.59)1.47 (0.85-2.52)
 Others0.97 (0.41-2.27)0.61 (0.22-1.68)NANANANA
Smoking history
 Never smoker1.01.01.0NA1.01.0
 Current smoker1.41 (0.95-2.1)0.82 (0.51-1.3)0.68 (0.35-1.32)NA1.23 (0.46-3.29)0.66 (0.23-1.93)
 Former smoker1.1 (0.87-1.4)0.84 (0.64-1.11)0.97 (0.66-1.41)NA1.21 (0.77-1.91)0.74 (0.44-1.26)
BMI1.01 (1-1.03)NA1.03 (1-1.05)1.03 (1-1.05)1 (0.97-1.03)0.99 (0.96-1.02)
Comorbidities
Diabetes mellitus1.82 (1.44-2.3)1.33 (1.02-1.75)1.09 (0.76-1.54)NA1.67 (1.09-2.58)1.27 (0.76-2.11)
COPD1.96 (1.47-2.6)1.92 (1.35-2.72)§1.41 (0.94-2.11)1.33 (0.84-2.1)1.74 (1.11-2.73)1.51 (0.88-2.6)
Chronic kidney disease1.76 (1.29-2.39)1.22 (0.86-1.73)1.83 (1.18-2.83)1.64 (1.02-2.62)1.94 (1.21-3.1)1.42 (0.83-2.43)
Chronic liver disease1.64 (1.22-2.22)1.31 (0.94-1.82)1.34 (0.93-1.93)NA1.29 (0.68-2.45)NA
Cardiovascular disease1.91 (1.51-2.41)1.52 (1.16-2)1.26 (0.84-1.88)1.03 (0.68-1.55)2.75 (1.66-4.55)2.21 (1.21-4.04)
Hypertension1.92 (1.51-2.45)1.32 (0.99-1.77)1.11 (0.75-1.66)NA1.38 (0.79-2.41)1.09 (0.52-2.26)
Allergic rhinitis0.66 (0.52-0.84)0.77 (0.59-1.01)1.83 (1.18-2.83)NA0.64 (0.37-1.11)0.73 (0.39-1.35)
Medications
ICS0.66 (0.52-0.84)0.92 (0.61-1.39)0.73 (0.45-1.19)NA0.85 (0.45-1.59)NA
ICS-LABA combination1.01 (0.76-1.33)1.08 (0.73-1.59)0.84 (0.54-1.3)NA0.65 (0.36-1.18)NA
Anticholinergic1.41 (0.86-2.3)0.74 (0.41-1.34)0.87 (0.42-1.79)NA1.32 (0.57-3.08)NA
 SABA
 SABA none1.01.01.01.01.01.0
 SABA-only0.65 (0.49-0.86)0.59 (0.43-0.8)§0.62 (0.39-1)1.6 (0.98-2.62)0.72 (0.37-1.39)0.74 (0.36-1.51)
 With controller0.76 (0.59-0.98)0.73 (0.48-1.11)0.46 (0.27-0.8)0.65 (0.42-1.02)0.83 (0.5-1.39)0.85 (0.47-1.53)
SABA-anticholinergic combination1.99 (1.31-3.01)1.74 (1.09-2.8)1.13 (0.65-1.97)NA1.54 (0.81-2.91)1.23 (0.61-2.48)

BMI, Body mass index; LABA, long-acting beta-agonist.

Values are OR (95% CI). Text in boldface indicates statistical significance after Bonferroni correction for multiple testing, with significance level set at P < .0016.

Age-stratified logistic regression analysis was applied to all individual variables except age.

Age-stratified multivariable analysis with the variables listed in the present table. Variables were chosen on the basis of P ≤ .1 calculated using Wilcoxon test, χ test, or Fisher exact test. “NA” indicates that the corresponding variable or variable category was not included for the multivariable analysis.

The OR and CI were reported for an increase in age by 10 years.

P < .001.

P < .05

P < .01

Risk factors associated with hospitalization, intensive care, and mortality among patients with a history of asthma and COVID-2019 BMI, Body mass index; LABA, long-acting beta-agonist. Values are OR (95% CI). Text in boldface indicates statistical significance after Bonferroni correction for multiple testing, with significance level set at P < .0016. Age-stratified logistic regression analysis was applied to all individual variables except age. Age-stratified multivariable analysis with the variables listed in the present table. Variables were chosen on the basis of P ≤ .1 calculated using Wilcoxon test, χ test, or Fisher exact test. “NA” indicates that the corresponding variable or variable category was not included for the multivariable analysis. The OR and CI were reported for an increase in age by 10 years. P < .001. P < .05 P < .01 Although obesity, chronic kidney disease, and marital status were significantly associated with increased risk of ICU admission compared with general inpatient hospitalization, they were not robust to Bonferroni correction. Similarly, cardiovascular disease (aOR, 2.21; 95% CI, 1.21-4.04; P < .01) and male sex were the only variables that predicted higher odds of mortality but did not meet the significance threshold for multiple testing. Several hospitalization risk factors for patients with asthma and COVID-19 reflect those identified in general populations of patients with COVID-19, including male sex, race, older age, and nonrespiratory comorbidities. Notably, male sex was a risk factor despite female predominance in COVID-19 testing and in positive diagnosis among patients with asthma. In distinguishing asthma within chronic respiratory disease categorization, we found that a comorbid diagnosis of COPD was a strong risk factor for hospitalization, and the only comorbidity that remained statistically significant after correction for multiple comparisons. Mild asthma managed with SABA alone was more common in patients triaged to outpatient care, and these patients were less likely to be hospitalized. In contrast, we found no differences in risk for hospitalization or ICU-level care with ICS or combined ICS-long-acting beta-agonist use. Asthma-specific variables did not predict ICU care or mortality, and the differences between risk for inpatient hospitalization and ICU admission are a compelling area for future investigation. MGB health system serves the largest volume of hospitalized patients with COVID-19 in New England. However, despite having an MGB primary care provider, some patients may have sought COVID-19 care out of our hospital system. Asthma prevalence in MGB COVID-19–positive patients (13.1%) is within range of chronic respiratory disease and/or COPD (4.6%-15.6%) rates from China COVID-19 studies , , and slightly higher than the asthma prevalence in a large New York City cohort (9%). Electronic health record prescription data are not linked to pharmacy fill data; future research could use administrative claims data to strengthen associations with baseline asthma medication use. Finally, a small number of patients remained hospitalized at the time of censoring, which may have led to underreporting of subsequent ICU admissions or deaths. Available data support that mortality was similar for patients with COVID-19 with or without asthma in the MGB outpatient and inpatient settings. Our findings highlight the importance of distinguishing asthma from chronic pulmonary diseases in COVID-19 research to establish an evidence base for risk evaluation and suggest that individuals with asthma-COPD overlap may be especially at risk. Further research examining the course of hospitalized patients is necessary to elucidate predictors of disease progression and clinical outcomes.
  14 in total

1.  User-centered design of a scalable, electronic health record-integrated remote symptom monitoring intervention for patients with asthma and providers in primary care.

Authors:  Robert S Rudin; Sofia Perez; Jorge A Rodriguez; Jessica Sousa; Savanna Plombon; Adriana Arcia; Dinah Foer; David W Bates; Anuj K Dalal
Journal:  J Am Med Inform Assoc       Date:  2021-10-12       Impact factor: 7.942

2.  The polyhedric reality of the interaction between COVID-19, asthma and inhaled corticosteroids.

Authors:  Francisco-Javier Gonzalez-Barcala; Juan-Jose Nieto-Fontarigo; Paula Mendez-Brea; Francisco-Javier Salgado
Journal:  ERJ Open Res       Date:  2022-05-30

3.  Epidemiology, Healthcare Resource Utilization, and Mortality of Asthma and COPD in COVID-19: A Systematic Literature Review and Meta-Analyses.

Authors:  David M G Halpin; Adrian Paul Rabe; Wei Jie Loke; Stacy Grieve; Patrick Daniele; Sanghee Hwang; Anna Forsythe
Journal:  J Asthma Allergy       Date:  2022-06-17

4.  The Risk of COVID-19 Related Hospitalsation, Intensive Care Unit Admission and Mortality in People With Underlying Asthma or COPD: A Systematic Review and Meta-Analysis.

Authors:  Shahina Pardhan; Samantha Wood; Megan Vaughan; Mike Trott
Journal:  Front Med (Lausanne)       Date:  2021-06-16

5.  Reply to: Kow CS et al. Are severe asthma patients at higher risk of developing severe outcomes from COVID-19?

Authors:  Enrico Heffler; Aikaterini Detoraki; Marco Contoli; Alberto Papi; Giovanni Paoletti; Giacomo Malipiero; Luisa Brussino; Claudia Crimi; Daniela Morrone; Marianna Padovani; Giuseppe Guida; Alberto Giovanni Gerli; Stefano Centanni; Gianenrico Senna; Pierluigi Paggiaro; Francesco Blasi; Giorgio Walter Canonica
Journal:  Allergy       Date:  2021-03       Impact factor: 13.146

6.  Uncontrolled asthma predicts severe COVID-19: a report from the Swedish National Airway Register.

Authors:  Johanna Karlsson Sundbaum; Jon R Konradsen; Lowie E G W Vanfleteren; Sten Axelsson Fisk; Christophe Pedroletti; Yvonne Sjöö; Jörgen Syk; Therese Sterner; Anne Lindberg; Alf Tunsäter; Fredrik Nyberg; Ann Ekberg-Jansson; Caroline Stridsman
Journal:  Ther Adv Respir Dis       Date:  2022 Jan-Dec       Impact factor: 5.158

Review 7.  The Possible Dual Role of the ACE2 Receptor in Asthma and Coronavirus (SARS-CoV2) Infection.

Authors:  Anna Cláudia Calvielli Castelo Branco; Maria Notomi Sato; Ricardo Wesley Alberca
Journal:  Front Cell Infect Microbiol       Date:  2020-09-23       Impact factor: 5.293

8.  Eosinophilia in Asthma Patients Is Protective Against Severe COVID-19 Illness.

Authors:  Denisa Ferastraoaru; Golda Hudes; Elina Jerschow; Sunit Jariwala; Merhunisa Karagic; Gabriele de Vos; David Rosenstreich; Manish Ramesh
Journal:  J Allergy Clin Immunol Pract       Date:  2021-01-23

9.  Eosinophil Responses at the Airway Epithelial Barrier during the Early Phase of Influenza A Virus Infection in C57BL/6 Mice.

Authors:  Meenakshi Tiwary; Robert J Rooney; Swantje Liedmann; Kim S LeMessurier; Amali E Samarasinghe
Journal:  Cells       Date:  2021-02-27       Impact factor: 6.600

10.  Chronic Respiratory Diseases and the Outcomes of COVID-19: A Nationwide Retrospective Cohort Study of 39,420 Cases.

Authors:  Wei-Jie Guan; Wen-Hua Liang; Ying Shi; Lan-Xia Gan; Hai-Bo Wang; Jian-Xing He; Nan-Shan Zhong
Journal:  J Allergy Clin Immunol Pract       Date:  2021-03-06
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