- Email: [email protected]
Relationship Between OSA Clinical Phenotypes and CPAP Treatment Outcomes
this misclassification may have impacted the study results. We commend the authors for the scope of their study and welcome its contribution to our understanding of the relationship between ICU staffing and mortality. We hope that the considerations raised in this letter will further our understanding of the complex relationships between organizational care factors and patient outcomes. Sarah E. Jolley, MD New Orleans, LA Catherine L. Hough, MD Seattle, WA AFFILIATIONS: From the Department of Pulmonary and Critical Care Medicine (Dr Jolley), Louisiana State University; and the Department of Pulmonary and Critical Care Medicine (Dr Hough), University of Washington. FINANCIAL/NONFINANCIAL DISCLOSURES: None declared. CORRESPONDENCE TO: Sarah E. Jolley, MD, Louisiana State University, 1901 Perdido St, Ste 3205, New Orleans, LA 70112; e-mail: [email protected] Copyright Ó 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved. DOI: http://dx.doi.org/10.1016/j.chest.2015.10.050
References 1. Kerlin MP, Harhay MO, Kahn JM, Halpern SD. Nighttime intensivist staffing, mortality, and limits on life support: a retrospective cohort study. Chest. 2015;147(4):951-958. 2. Kahn JM, Kramer AA, Rubenfeld GD. Transferring critically ill patients out of hospital improves the standardized mortality ratio: a simulation study. Chest. 2007;131(1):68-75. 3. Angus DC, Shorr AF, White A, Dremsizov TT, Schmitz RJ, Kelley MA; Committee on Manpower for Pulmonary and Critical Care Societies (COMPACCS). Critical care delivery in the United States: distribution of services and compliance with Leapfrog recommendations. Crit Care Med. 2006;34(4):1016-1024. 4. Reineck LA, Le TQ, Seymour CW, Barnato AE, Angus DC, Kahn JM. Effect of public reporting on intensive care unit discharge destination and outcomes. Ann Am Thorac Soc. 2015;12(1):57-63. 5. Venkataraman R, Ramakrishnan N. Outcomes related to telemedicine in the intensive care unit: what we know and would like to know. Crit Care Clin. 2015;31(2):225-237.
Relationship Between OSA Clinical Phenotypes and CPAP Treatment Outcomes To the Editor:
There is a growing awareness of heterogeneity between patients with OSA in terms of symptoms and comorbidities.1 The aim of this study was to identify clinically meaningful OSA phenotypes by means of cluster analysis and to evaluate their relationship with relevant CPAP outcomes.
288 Correspondence
Latent class analysis1,2 was used to identify phenotypes based on 13 clinically relevant variables in 5,983 patients with newly diagnosed moderate-to-severe OSA from the Institut de Recherche en Santé Respiratoire des Pays de la Loire multicenter prospective sleep cohort. Further methodological details are found in e-Appendix 1 and e-Table 1. Five distinct clusters with marked clinical differences were identified (Fig 1, e-Tables 2, 3). Cluster 1 was characterized by a marked female predominance, high rates of insomnia complaints, depressive symptoms, obesity, and associated comorbidities. Patients from clusters 2 and 3 had marked typical nocturnal and diurnal OSA symptoms and frequent depressive symptoms. Cluster 2 differed from cluster 3 by a male predominance and more frequent comorbidities. Patients in cluster 4 had nocturnal OSA symptoms and insomnia complaints but a low prevalence of excessive daytime sleepiness, depressive symptoms, and comorbidities. Cluster 5 included a marked predominance of minimally symptomatic male patients older than 65 years with a high rate of comorbidities. Treatment outcomes were then compared across clusters in the subgroup of patients in whom CPAP had been prescribed for at least 6 months. A strong agreement (kappa 0.92) was observed between OSA clusters identified in the entire baseline population (n ¼ 5,983) and in the CPAP follow-up population (n ¼ 3,090; e-Fig 1). CPAP treatment success was defined as daily CPAP use $4 h and either a decrease in Epworth sleepiness score (ESS) $4 points in patients with a baseline value $11 and/or an increase of at least 7 points in the energy/vitality component score of the Short Form 36 questionnaire. After adjustment for socioeconomic status, baseline apnea-hypopnea index, and ESS, patients from clusters 1, 4, and 5 that we propose to label as “female OSA,” “mildly symptomatic OSA,” and “comorbid OSA,” respectively, had a lower likelihood of CPAP treatment success than patients with “severe OSA syndrome” from cluster 3 (Table 1, e-Table 4). Our findings suggest that cluster analysis provides an opportunity for broader than usual pretreatment clinical characterization of patients with OSA. The longitudinal association between OSA clusters and CPAP treatment outcomes remained significant after adjusting for criteria commonly used to assess OSA severity and to prescribe CPAP therapy, including socioeconomic status, apneahypopnea index, and ESS.3-5 These findings suggest that the proposed subtype classification provides relevant
[
149#1 CHEST JANUARY 2016
]
Cluster 1 n = 848 (14.2%)
Cluster 2
Cluster 3
Cluster 4
Cluster 5
n = 900 (15.1%) n = 1090 (18.2%) n = 1918 (32.0%) n = 1227 (20.5%)
Obesity 100%
Age >65 years Gender : female Complaint of insomnia
80%
Loud snoring Stopped breathing
60%
Unrested upon waking Headache upon waking
40%
Depressive symptoms Excessive daytime sleepiness
20%
Hypertension Cardiovascular disease
0%
Diabetes
Figure 1 – Prevalence of each variable according to the clusters identified by latent class analysis in 5,983 patients with moderate-to-severe OSA that was newly diagnosed. Each colored line represents a variable with prevalence ranging from 0% (yellow) to 100% (red). TABLE 1 ]
Unadjusted and Adjusted ORs (95% CI) for the Success of CPAP Treatment Associated With OSA Clusters in the Subgroup of Patients in Whom CPAP Had Been Prescribed for at Least 6 Months (n ¼ 3,090) Adjusted OR (95% CI) Unadjusted OR (95% CI)
Model 1
Model 2
Model 3
Cluster 1
0.45 (0.35-0.57)
0.45 (0.34-0.59)
0.43 (0.33-0.57)
0.66 (0.49-0.89)
Cluster 2
0.87 (0.69-1.10)
0.80 (0.61-1.03)
0.74 (0.57-0.96)
0.85 (0.65-1.12)
Cluster 3
1.00
1.00
1.00
1.00
Cluster 4
0.47 (0.38-0.58)
0.43 (0.35-0.54)
0.43 (0.35-0.54)
0.66 (0.52-0.84)
Cluster 5
0.25 (0.20-0.32)
0.21 (0.16-0.28)
0.20 (0.15-0.27)
0.36 (0.26-0.49)
Model 1 ¼ adjusted for marital, educational, and employment status; model 2 ¼ adjusted for marital, educational, and employment status and apneahypopnea index; model 3 ¼ adjusted for marital, educational, and employment status; apnea-hypopnea index; and baseline Epworth sleepiness score.
prognostic information regarding CPAP treatment outcomes not provided by these clinical criteria alone. Frédéric Gagnadoux, MD, PhD Angers, France Marc Le Vaillant, PhD Villejuif, France Audrey Paris, MD, PhD LeMans, France Thierry Pigeanne, MD Olonne sur Mer, France Laurence Leclair-Visonneau, MD Nantes, France Acya Bizieux-Thaminy, MD La Roche sur Yon, France Claire Alizon, MD Cholet, France Marie-Pierre Humeau, MD Nantes, France Xuan-Lan Nguyen, MD
journal.publications.chestnet.org
Paris, France Béatrice Rouault, MD Machecoul, France Wojciech Trzepizur, MD, PhD Angers, France Nicole Meslier, MD Angers, France on behalf of the Institut de Recherche en Santé Respiratoire des Pays de la Loire Sleep Cohort Group* AFFILIATIONS: From the Département de Pneumologie (Drs Gagnadoux, Trzepizur, and Meslier), Université d’Angers, Centre Hospitalier Universitaire; INSERM 1063; CERMES (Dr Le Vaillant), CNRS UMR8211- INSERM U988-EHESS; Service de Pneumologie (Dr Paris), Centre Hospitalier; Unité de Pneumologie (Dr Pigeanne), Pôle santé des Olonnes; Institut du Thorax, Pneumologie (Dr Leclair-Visonneau), Hôpital Laennec; Service de Pneumologie (Dr Bizieux-Thaminy), Centre Hospitalier; Service de Pneumologie (Dr Alizon), Centre Hospitalier; Pneumologie (Dr Humeau), Nouvelles Cliniques Nantaises; Centre d’Etude et de Traitement des Troubles du Sommeil de Saint-Antoine (Dr Nguyen), Hôpital Saint-Antoine, Groupe Hospitalier de l’Est Parisien; Service de Pneumologie (Dr Rouault), Centre Hospitalier Loire-Vendée Océan.
289
AUTHOR CONTRIBUTIONS: F. G., N. M., and M .L. V. researched data, contributed to discussions, wrote the manuscript, and reviewed/edited the manuscript. A. B. T., A. P., B. R., C. A., L. L. V., M. P. H., T. P., W. T., and X. L. N. researched data, contributed to discussions, and reviewed/edited the manuscript. FINANCIAL/NONFINANCIAL DISCLOSURES:
None declared.
*WRITING COMMITTEE MEMBERS FOR [The Institut de Recherche en Santé Respiratoire des Pays de la Loire Sleep Cohort Group]: Centre Hospitalier Universitaire, Angers, France: Christine Person, Pascaline Priou; Centre Hospitalier, Le Mans, France: François Goupil, Olivier Molinier; Centre Hospitalier, La Roche sur Yon, France: Philippe Breton, Kamel Berkani; Pôle santé des Olonnes, Olonne sur Mer, France: Marie Langelot-Richard; Centre Hospitalier Universitaire, Nantes, France: Sylvaine Chollet, Sandrine Jaffre, Frédéric Corne; Nouvelles Cliniques Nantaises, Nantes, France: Marie-Pierre Humeau, Marc Normand de la Tranchade; ALTADIR, Beaucouzé, France: Jean-Louis Racineux, Christelle Gosselin; CERMES, CNRS UMR8211 - INSERM U988 EHESS, site CNRS, Villejuif, France: Nathalie Pelletier-Fleury. CORRESPONDENCE TO: Frédéric Gagnadoux, MD, PhD, Département de Pneumologie, Université d’Angers, CHU Angers, 4 rue Larrey, 49033 Angers Cedex, France; e-mail: frgagnadoux@ chu-angers.fr ADDITIONAL INFORMATION: The e-Appendix, e-Figures, and e-Tables can be found in the Supplemental Materials section of the online article. Copyright Ó 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved. DOI: http://dx.doi.org/10.1016/j.chest.2015.09.032
References 1. Ye L, Pien GW, Ratcliffe SJ, et al. The different clinical faces of obstructive sleep apnoea: a cluster analysis. Eur Respir J. 2014;44(6): 1600-1607. 2. Sipsma HL, Falb KL, Willie T, et al. Violence against Congolese refugee women in Rwanda and mental health: a cross-sectional study using latent class analysis. BMJ Open. 2015;5(4):e006299. 3. Sawyer AM, Gooneratne NS, Marcus CL, Ofer D, Richards KC, Weaver TE. A systematic review of CPAP adherence across age groups: clinical and empiric insights for developing CPAP adherence interventions. Sleep Med Rev. 2011;15(6):343-356. 4. Crawford MR, Espie CA, Bartlett DJ, Grunstein RR. Integrating psychology and medicine in CPAP adherence—new concepts? Sleep Med Rev. 2013;18(2):123-139. 5. Gagnadoux F, Le Vaillant M, Goupil F, et al. Influence of marital status and employment status on long-term adherence with continuous positive airway pressure in sleep apnea patients. PLoS One. 2011;6(8):e22503.
Choice of Imaging Studies in Acutely Ill Pregnant Women To the Editor:
The case report in CHEST (June 2015) by Daglian and Patrawalla1 highlights the challenge of emergency care of the obstetric patient. While the innovative use of lung ultrasonography for rapid and accurate diagnosis in respiratory failure is promising and could be particularly beneficial to the pregnant patient, we would like to suggest redirecting strategies for selecting diagnostic studies in complicated pregnant patients to focus on
290 Correspondence
timely diagnosis and avoiding undue concerns about safety risk. As in the nonpregnant population, lung ultrasonography would be most attractive if it has good positive and negative predictive value, is safe, and there is expertise with using and interpreting it locally. It is common for providers to withhold tests in a pregnant patient due to presumed risks to the fetus. This may lead to delays in diagnosis as well as diagnostic and therapeutic errors, resulting in similar or worse harm. In this case report, chest radiograph was initially withheld, and pulmonary embolism (PE) was excluded based on Doppler testing of the legs alone. Though PE was unlikely in this patient, clinicians had a high enough suspicion to empirically initiate anticoagulation treatment, bypassing diagnostic testing. Such practices are considered substandard care in the nonpregnant population; the same should apply to pregnancy. In the United States, reducing maternal mortality and morbidity remains a challenge, with the maternal mortality ratio doubling between 1990 and 20132 in part due to inconsistent obstetric practice across hospitals.2 Peripartum VTE, still one of the leading causes of maternal deaths in the United States, has been identified as one of the priority bundles by the National Partnership for Maternal Safety.3 By standardizing care through the use of evidence-based national guidelines, we can decrease PE-related maternal deaths as seen in the United Kingdom.4 Current guidelines recommend that in pregnant women with suspected PE and no signs and symptoms of DVT (as in this case), studies of the pulmonary vasculature should not be delayed.5,6 This includes chest radiograph followed by either a CT angiogram or a ventilation perfusion scan. While the risk of teratogenicity requires a radiation threshold of at least 5 rad,7 a CT angiogram or chest radiograph exposes the fetus to very small amounts of radiation (0.01 rad and 0.0002 rad, respectively).7 Lung ultrasonography may prove to be a valuable tool, however, it should not replace our current standard of care in pregnant women until it becomes fully validated both in and outside of pregnancy. Tabassum Firoz, MD Vancouver, BC, Canada Margaret A. Miller, MD Ghada Bourjeily, MD Providence, RI
[
149#1 CHEST JANUARY 2016
]
References 1. Kerlin MP, Harhay MO, Kahn JM, Halpern SD. Nighttime intensivist staffing, mortality, and limits on life support: a retrospective cohort study. Chest. 2015;147(4):951-958. 2. Kahn JM, Kramer AA, Rubenfeld GD. Transferring critically ill patients out of hospital improves the standardized mortality ratio: a simulation study. Chest. 2007;131(1):68-75. 3. Angus DC, Shorr AF, White A, Dremsizov TT, Schmitz RJ, Kelley MA; Committee on Manpower for Pulmonary and Critical Care Societies (COMPACCS). Critical care delivery in the United States: distribution of services and compliance with Leapfrog recommendations. Crit Care Med. 2006;34(4):1016-1024. 4. Reineck LA, Le TQ, Seymour CW, Barnato AE, Angus DC, Kahn JM. Effect of public reporting on intensive care unit discharge destination and outcomes. Ann Am Thorac Soc. 2015;12(1):57-63. 5. Venkataraman R, Ramakrishnan N. Outcomes related to telemedicine in the intensive care unit: what we know and would like to know. Crit Care Clin. 2015;31(2):225-237.
Relationship Between OSA Clinical Phenotypes and CPAP Treatment Outcomes To the Editor:
There is a growing awareness of heterogeneity between patients with OSA in terms of symptoms and comorbidities.1 The aim of this study was to identify clinically meaningful OSA phenotypes by means of cluster analysis and to evaluate their relationship with relevant CPAP outcomes.
288 Correspondence
Latent class analysis1,2 was used to identify phenotypes based on 13 clinically relevant variables in 5,983 patients with newly diagnosed moderate-to-severe OSA from the Institut de Recherche en Santé Respiratoire des Pays de la Loire multicenter prospective sleep cohort. Further methodological details are found in e-Appendix 1 and e-Table 1. Five distinct clusters with marked clinical differences were identified (Fig 1, e-Tables 2, 3). Cluster 1 was characterized by a marked female predominance, high rates of insomnia complaints, depressive symptoms, obesity, and associated comorbidities. Patients from clusters 2 and 3 had marked typical nocturnal and diurnal OSA symptoms and frequent depressive symptoms. Cluster 2 differed from cluster 3 by a male predominance and more frequent comorbidities. Patients in cluster 4 had nocturnal OSA symptoms and insomnia complaints but a low prevalence of excessive daytime sleepiness, depressive symptoms, and comorbidities. Cluster 5 included a marked predominance of minimally symptomatic male patients older than 65 years with a high rate of comorbidities. Treatment outcomes were then compared across clusters in the subgroup of patients in whom CPAP had been prescribed for at least 6 months. A strong agreement (kappa 0.92) was observed between OSA clusters identified in the entire baseline population (n ¼ 5,983) and in the CPAP follow-up population (n ¼ 3,090; e-Fig 1). CPAP treatment success was defined as daily CPAP use $4 h and either a decrease in Epworth sleepiness score (ESS) $4 points in patients with a baseline value $11 and/or an increase of at least 7 points in the energy/vitality component score of the Short Form 36 questionnaire. After adjustment for socioeconomic status, baseline apnea-hypopnea index, and ESS, patients from clusters 1, 4, and 5 that we propose to label as “female OSA,” “mildly symptomatic OSA,” and “comorbid OSA,” respectively, had a lower likelihood of CPAP treatment success than patients with “severe OSA syndrome” from cluster 3 (Table 1, e-Table 4). Our findings suggest that cluster analysis provides an opportunity for broader than usual pretreatment clinical characterization of patients with OSA. The longitudinal association between OSA clusters and CPAP treatment outcomes remained significant after adjusting for criteria commonly used to assess OSA severity and to prescribe CPAP therapy, including socioeconomic status, apneahypopnea index, and ESS.3-5 These findings suggest that the proposed subtype classification provides relevant
[
149#1 CHEST JANUARY 2016
]
Cluster 1 n = 848 (14.2%)
Cluster 2
Cluster 3
Cluster 4
Cluster 5
n = 900 (15.1%) n = 1090 (18.2%) n = 1918 (32.0%) n = 1227 (20.5%)
Obesity 100%
Age >65 years Gender : female Complaint of insomnia
80%
Loud snoring Stopped breathing
60%
Unrested upon waking Headache upon waking
40%
Depressive symptoms Excessive daytime sleepiness
20%
Hypertension Cardiovascular disease
0%
Diabetes
Figure 1 – Prevalence of each variable according to the clusters identified by latent class analysis in 5,983 patients with moderate-to-severe OSA that was newly diagnosed. Each colored line represents a variable with prevalence ranging from 0% (yellow) to 100% (red). TABLE 1 ]
Unadjusted and Adjusted ORs (95% CI) for the Success of CPAP Treatment Associated With OSA Clusters in the Subgroup of Patients in Whom CPAP Had Been Prescribed for at Least 6 Months (n ¼ 3,090) Adjusted OR (95% CI) Unadjusted OR (95% CI)
Model 1
Model 2
Model 3
Cluster 1
0.45 (0.35-0.57)
0.45 (0.34-0.59)
0.43 (0.33-0.57)
0.66 (0.49-0.89)
Cluster 2
0.87 (0.69-1.10)
0.80 (0.61-1.03)
0.74 (0.57-0.96)
0.85 (0.65-1.12)
Cluster 3
1.00
1.00
1.00
1.00
Cluster 4
0.47 (0.38-0.58)
0.43 (0.35-0.54)
0.43 (0.35-0.54)
0.66 (0.52-0.84)
Cluster 5
0.25 (0.20-0.32)
0.21 (0.16-0.28)
0.20 (0.15-0.27)
0.36 (0.26-0.49)
Model 1 ¼ adjusted for marital, educational, and employment status; model 2 ¼ adjusted for marital, educational, and employment status and apneahypopnea index; model 3 ¼ adjusted for marital, educational, and employment status; apnea-hypopnea index; and baseline Epworth sleepiness score.
prognostic information regarding CPAP treatment outcomes not provided by these clinical criteria alone. Frédéric Gagnadoux, MD, PhD Angers, France Marc Le Vaillant, PhD Villejuif, France Audrey Paris, MD, PhD LeMans, France Thierry Pigeanne, MD Olonne sur Mer, France Laurence Leclair-Visonneau, MD Nantes, France Acya Bizieux-Thaminy, MD La Roche sur Yon, France Claire Alizon, MD Cholet, France Marie-Pierre Humeau, MD Nantes, France Xuan-Lan Nguyen, MD
journal.publications.chestnet.org
Paris, France Béatrice Rouault, MD Machecoul, France Wojciech Trzepizur, MD, PhD Angers, France Nicole Meslier, MD Angers, France on behalf of the Institut de Recherche en Santé Respiratoire des Pays de la Loire Sleep Cohort Group* AFFILIATIONS: From the Département de Pneumologie (Drs Gagnadoux, Trzepizur, and Meslier), Université d’Angers, Centre Hospitalier Universitaire; INSERM 1063; CERMES (Dr Le Vaillant), CNRS UMR8211- INSERM U988-EHESS; Service de Pneumologie (Dr Paris), Centre Hospitalier; Unité de Pneumologie (Dr Pigeanne), Pôle santé des Olonnes; Institut du Thorax, Pneumologie (Dr Leclair-Visonneau), Hôpital Laennec; Service de Pneumologie (Dr Bizieux-Thaminy), Centre Hospitalier; Service de Pneumologie (Dr Alizon), Centre Hospitalier; Pneumologie (Dr Humeau), Nouvelles Cliniques Nantaises; Centre d’Etude et de Traitement des Troubles du Sommeil de Saint-Antoine (Dr Nguyen), Hôpital Saint-Antoine, Groupe Hospitalier de l’Est Parisien; Service de Pneumologie (Dr Rouault), Centre Hospitalier Loire-Vendée Océan.
289
AUTHOR CONTRIBUTIONS: F. G., N. M., and M .L. V. researched data, contributed to discussions, wrote the manuscript, and reviewed/edited the manuscript. A. B. T., A. P., B. R., C. A., L. L. V., M. P. H., T. P., W. T., and X. L. N. researched data, contributed to discussions, and reviewed/edited the manuscript. FINANCIAL/NONFINANCIAL DISCLOSURES:
None declared.
*WRITING COMMITTEE MEMBERS FOR [The Institut de Recherche en Santé Respiratoire des Pays de la Loire Sleep Cohort Group]: Centre Hospitalier Universitaire, Angers, France: Christine Person, Pascaline Priou; Centre Hospitalier, Le Mans, France: François Goupil, Olivier Molinier; Centre Hospitalier, La Roche sur Yon, France: Philippe Breton, Kamel Berkani; Pôle santé des Olonnes, Olonne sur Mer, France: Marie Langelot-Richard; Centre Hospitalier Universitaire, Nantes, France: Sylvaine Chollet, Sandrine Jaffre, Frédéric Corne; Nouvelles Cliniques Nantaises, Nantes, France: Marie-Pierre Humeau, Marc Normand de la Tranchade; ALTADIR, Beaucouzé, France: Jean-Louis Racineux, Christelle Gosselin; CERMES, CNRS UMR8211 - INSERM U988 EHESS, site CNRS, Villejuif, France: Nathalie Pelletier-Fleury. CORRESPONDENCE TO: Frédéric Gagnadoux, MD, PhD, Département de Pneumologie, Université d’Angers, CHU Angers, 4 rue Larrey, 49033 Angers Cedex, France; e-mail: frgagnadoux@ chu-angers.fr ADDITIONAL INFORMATION: The e-Appendix, e-Figures, and e-Tables can be found in the Supplemental Materials section of the online article. Copyright Ó 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved. DOI: http://dx.doi.org/10.1016/j.chest.2015.09.032
References 1. Ye L, Pien GW, Ratcliffe SJ, et al. The different clinical faces of obstructive sleep apnoea: a cluster analysis. Eur Respir J. 2014;44(6): 1600-1607. 2. Sipsma HL, Falb KL, Willie T, et al. Violence against Congolese refugee women in Rwanda and mental health: a cross-sectional study using latent class analysis. BMJ Open. 2015;5(4):e006299. 3. Sawyer AM, Gooneratne NS, Marcus CL, Ofer D, Richards KC, Weaver TE. A systematic review of CPAP adherence across age groups: clinical and empiric insights for developing CPAP adherence interventions. Sleep Med Rev. 2011;15(6):343-356. 4. Crawford MR, Espie CA, Bartlett DJ, Grunstein RR. Integrating psychology and medicine in CPAP adherence—new concepts? Sleep Med Rev. 2013;18(2):123-139. 5. Gagnadoux F, Le Vaillant M, Goupil F, et al. Influence of marital status and employment status on long-term adherence with continuous positive airway pressure in sleep apnea patients. PLoS One. 2011;6(8):e22503.
Choice of Imaging Studies in Acutely Ill Pregnant Women To the Editor:
The case report in CHEST (June 2015) by Daglian and Patrawalla1 highlights the challenge of emergency care of the obstetric patient. While the innovative use of lung ultrasonography for rapid and accurate diagnosis in respiratory failure is promising and could be particularly beneficial to the pregnant patient, we would like to suggest redirecting strategies for selecting diagnostic studies in complicated pregnant patients to focus on
290 Correspondence
timely diagnosis and avoiding undue concerns about safety risk. As in the nonpregnant population, lung ultrasonography would be most attractive if it has good positive and negative predictive value, is safe, and there is expertise with using and interpreting it locally. It is common for providers to withhold tests in a pregnant patient due to presumed risks to the fetus. This may lead to delays in diagnosis as well as diagnostic and therapeutic errors, resulting in similar or worse harm. In this case report, chest radiograph was initially withheld, and pulmonary embolism (PE) was excluded based on Doppler testing of the legs alone. Though PE was unlikely in this patient, clinicians had a high enough suspicion to empirically initiate anticoagulation treatment, bypassing diagnostic testing. Such practices are considered substandard care in the nonpregnant population; the same should apply to pregnancy. In the United States, reducing maternal mortality and morbidity remains a challenge, with the maternal mortality ratio doubling between 1990 and 20132 in part due to inconsistent obstetric practice across hospitals.2 Peripartum VTE, still one of the leading causes of maternal deaths in the United States, has been identified as one of the priority bundles by the National Partnership for Maternal Safety.3 By standardizing care through the use of evidence-based national guidelines, we can decrease PE-related maternal deaths as seen in the United Kingdom.4 Current guidelines recommend that in pregnant women with suspected PE and no signs and symptoms of DVT (as in this case), studies of the pulmonary vasculature should not be delayed.5,6 This includes chest radiograph followed by either a CT angiogram or a ventilation perfusion scan. While the risk of teratogenicity requires a radiation threshold of at least 5 rad,7 a CT angiogram or chest radiograph exposes the fetus to very small amounts of radiation (0.01 rad and 0.0002 rad, respectively).7 Lung ultrasonography may prove to be a valuable tool, however, it should not replace our current standard of care in pregnant women until it becomes fully validated both in and outside of pregnancy. Tabassum Firoz, MD Vancouver, BC, Canada Margaret A. Miller, MD Ghada Bourjeily, MD Providence, RI
[
149#1 CHEST JANUARY 2016
]