Corrections7 May 2019

Correction: Error in Letters in 5 February 2019

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    Two letters in the 5 February issue had the wrong figures (1, 2). The corrected letters appear below.

    Abetalipoproteinemia From Previously Unreported Gene Mutations

    Background: Abetalipoproteinemia is a rare, autosomal recessive disorder that requires mutations in both alleles of the gene for microsomal triglyceride transfer protein (MTTP). The mutations cause improper packaging and secretion of lipoprotein particles that contain apolipoprotein B from enterocytes and hepatocytes. These abnormalities lead to intracellular lipid retention and reductions in plasma levels of chylomicrons and very-low-density lipoprotein. Lipids and lipid-soluble vitamins are therefore malabsorbed, which causes retinal degeneration and eventual blindness followed by neuropathy and coagulopathy that are often severe and debilitating. Clinical diagnosis of abetalipoproteinemia is difficult because of its similarity to 2 related conditions, hypobetalipoproteinemia and chylomicron retention disease (Figure 1). Definitive diagnosis involves identifying the mutations in both alleles of the MTTP gene (1–4).

    Figure 1. Pathophysiology of abetalipoproteinemia.

    The combination of decreased plasma levels of chylomicrons and VLDL can be produced by defects in the gene for either MTTP (*) or apolipoprotein B (APOB) (†). Defects in MTTP lead to abetalipoproteinemia, and defects in APOB lead to hypobetalipoproteinemia. Defects in the gene for secretion associated Ras related guanosine triphosphatase 1B (SAR1B) (indicated by a double dagger) impair production of chylomicrons but not VLDL and lead to chylomicron retention disease. apo = apolipoprotein; MTTP = microsomal triglyceride transfer protein; TG = triglycerides; VLDL = very-low-density lipoprotein.

    Objective: To describe a patient with abetalipoproteinemia caused by previously unrecognized mutations in MTTP.

    Case Report: An otherwise-healthy 18-year-old woman presented with mild vision impairment and inability to see in dim light (nyctalopia). As an infant, the patient was diagnosed with failure to thrive, easy vomiting, and fatty stools. Results of diagnostic laparotomy at 3 months of age were normal. Dietary fats were eliminated by trial and error, and she developed normally except for night blindness first noticed at age 4 years and a mild bleeding diathesis that was recognized later.

    Findings on ophthalmologic examination were compatible with long-standing vitamin A deficiency. The patient had keratinization of the bulbar conjunctiva (Bitot spots), indicating extreme dry-eye syndrome (Figure 2), and small, white dots in the retinas. Full-field flash electroretinography showed only residual rod-specific function with normal amplitudes but delayed implicit times for cone-specific responses. Optical coherence tomography showed structural abnormalities of the outer retinal layers, indicating some loss of photoreceptor and retinal pigment epithelial cells. Additional physical examination revealed upper- and lower-extremity areflexia.

    Figure 2. Temporal conjunctival surface of the left eye showing an extreme sicca syndrome with vacuolization and keratinization (Bitot spots).

    Laboratory studies consistently showed severe deficiencies of the fat-soluble vitamins (vitamin A level, <0.42 µmol/L; vitamin E level, < 7µmol/L; and an elevated international normalized ratio of 2.67). Because the patient was receiving oral vitamin D replacement therapy, 25-hydroxyvitamin D levels were normal at 96 nmol/L. She also had extreme hypocholesterolemia (total cholesterol level, 0.78 mmol/L [30 mg/dL]; undetectable levels of low-density lipoprotein cholesterol; high-density lipoprotein cholesterol level, 0.78 mmol/L [30 mg/dL]), and triglyceride levels were almost completely absent. We attributed the triglyceride level of 0.02 mmol/L [2 mg/dL] to background glycerols without triglycerides. The patient also had low levels of apolipoprotein B (0.2 g/L [normal range, 0.6 to 1.3 g/L]), apolipoprotein A1 (0.8 g/L [normal range, 1.3 to 2.2 g/L]), and apolipoprotein A2 (0.1 g/L [normal range, 0.3 to 0.5 g/L]). Free fatty acid levels and thyroid function were normal. Blood smears showed pronounced acanthocytosis, and there were signs of increased erythrocyte turnover rates (reticulocyte count, 2.2%, and hemoglobin A1c level, 3.8%).

    Mild hepatic steatosis was noted on ultrasonography; gastroduodenoscopy showed a marked, pale-yellow discoloration of the small-bowel mucosa, about which we have written previously (5). Results of duodenal biopsies confirmed lipid-filled vacuoles in the enterocytes. Electromyography showed polyneuropathy. Bone densitometry revealed osteopenia but not osteomalacia or rickets. All of these findings were compatible with abetalipoproteinemia.

    The patient was the only child of nonconsanguineous parents and the only person affected in either family. Next-generation sequencing was done on the coding exons and at least 20 flanking nucleotides of 29 genes involved in lipoprotein metabolism (including APOB, PCSK9, ANGPTL3, MTTP, MYLIP, and SAR1B) to detect point mutations and small deletions or insertions. Further analysis was done to identify large deletions or duplications. The patient's DNA revealed a variant in intron 4 of MTTP, c.393 + 1A>T, which affects the 3' acceptor splice site, and a heterozygous complete deletion of MTTP (reference sequence NM_000253.3). One variant was confirmed in the DNA of the patient's mother and the other in the DNA of her father. To our knowledge, neither variant has been described before or identified in index cases in our laboratory or in the approximately 120 000 alleles of the Exome Aggregation Consortium database (

    Discussion: The patient is being treated with a low-fat diet; intravenous supplementation of essential fatty acids; and large-dose lipophilic vitamins, including intravenous doses when necessary. We expect that this regimen will slow disease progression and help preserve neurologic, ophthalmologic, and bone function (3).

    Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at

    This article was published at on 6 November 2018.

    Xavier-Philippe Aers, MD*

    Bart P. Leroy, MD, PhD*

    Ghent University Hospital and Ghent University, Ghent, Belgium

    Joep C. Defesche, PhD

    Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands

    Samyah Shadid, MD, PhD

    Ghent University Hospital and Ghent University, Ghent, Belgium

    * Drs. Aers and Leroy contributed equally to this work.


    1. Burnett JR, Bell DA, Hooper AJ, Hegele RA. Clinical utility gene card for: abetalipoproteinaemia—update 2014. Eur J Hum Genet. 2015;23. [PMID: 25335492] doi:10.1038/ejhg.2014.224

    2. Lee J, Hegele RA. Abetalipoproteinemia and homozygous hypobetalipoproteinemia: a framework for diagnosis and management. J Inherit Metab Dis. 2014;37:333-9. [PMID: 24288038] doi:10.1007/s10545-013-9665-4

    3. Paquette M, Dufour R, Hegele RA, Baass A. A tale of 2 cousins: an atypical and a typical case of abetalipoproteinemia. J Clin Lipidol. 2016;10:1030-4. [PMID: 27578136] doi:10.1016/j.jacl.2016.01.003

    4. Peretti N, Sassolas A, Roy CC, Deslandres C, Charcosset M, Castagnetti J, et al; Department of Nutrition-Hepatogastroenterology, Hôpital Femme Mère Enfant, Bron, Université Lyon 1. Guidelines for the diagnosis and management of chylomicron retention disease based on a review of the literature and the experience of two centers. Orphanet J Rare Dis. 2010;5:24. [PMID: 20920215] doi:10.1186/1750-1172-5-24

    5. Desomer L, De Vos M, De Looze D. Fat accumulation in enterocytes: a key to the diagnosis of abetalipoproteinemia or homozygous hypobetalipoproteinemia. Endoscopy. 2015;47 Suppl 1 UCTN:E223-4. [PMID: 26062159] doi:10.1055/s-0034-1391832

    Population Trends in Intensive Care Unit Admissions in the United States Among Medicare Beneficiaries, 2006–2015

    Background: Admission to the intensive care unit (ICU) is costly and strains health system resources (1). Accurate estimates of population-level ICU admission rates could aid in disaster planning, training a suitable critical care workforce, and targeting policy interventions to reduce admissions that are low-value or discordant with patient preferences.

    Objective: To overcome limitations of prior estimates of ICU use (2) by evaluating nationally representative data and examining geographic differences in admission incidence.

    Methods and Findings: Using the entire Medicare Provider Analysis and Review file, we evaluated all hospitalizations involving acute and ICU care (including cardiac intensive care but not intermediate care [3]) between 2006 and 2015 among Medicare fee-for-service beneficiaries aged 65 years or older. We calculated person-years of coverage using the Medicare Provider Analysis and Review Denominator file.

    We used data from all 50 states; Washington, DC; and Puerto Rico to assess the relationship between the incidence of ICU admissions and the number of total ICU beds (that is, medical, surgical, and cardiac), estimated using the American Hospital Association Annual Survey Database. For among-state comparisons, we adjusted ICU admission rates for each state's age and sex distributions using 2010 U.S. Census data. We tested changes over time for counts and proportions using simple Poisson regression and the χ2 test for trend, respectively. We reported CIs around count estimates using 95% 2-sided Poisson values. Analyses were performed using SAS (SAS Institute) and the R programming language (R Foundation for Statistical Computing). The Institutional Review Board of the University of Pennsylvania approved this study.

    We analyzed claims from 88 402 008 hospitalizations, 14 787 690 (16.7%) of which were associated with ICU care during 289 391 446 person-years of coverage. The ICU admission rate was 6117 per 100 000 person-years (95% CI, 5965 to 6272) in 2006 and decreased to 4247 per 100 000 person-years (CI, 4120 to 4377) in 2015 (P < 0.001). The proportion of hospitalizations that included ICU care also decreased during this period from 17.0% to 16.3% (P < 0.001).

    In 2015, we observed a nearly 3-fold difference among state-level ICU admission rates, which ranged from 2117 per 100 000 person-years (CI, 2027 to 2209) in Hawaii to 6312 per 100 000 person-years (CI, 6157 to 6470) in Mississippi. Intensive care unit admission rates decreased in all states except Nebraska, where they increased from 3798 per 100 000 person-years (CI, 3678 to 3921) in 2006 to 3992 per 100 000 person-years (CI, 3869 to 4118) in 2015.

    In 2006, the state-level ICU admission rate and number of total beds were positively associated with up to approximately 20 ICU beds per 100 000 person-years (Figure 1), with no clear association at greater bed capacities. The association between ICU admissions and number of beds was more strongly positive in 2015, with a monotonically increasing relationship except for 2 regions (Washington, DC, and North Dakota) with many beds and few admissions. Although the national ICU bed count increased by 11.4% between 2006 and 2015, state-level changes ranged from −38.1% (Rhode Island) to 54.4% (Washington). No association was observed between the percentage of change in ICU beds and admissions across states (Figure 2).

    Figure 1. ICU admissions and beds over time, by state.

    Relationship between age- and sex-adjusted ICU admission rates among Medicare fee-for-service beneficiaries aged ≥65 y in each state and total ICU beds in 2006 and 2015. Lines represent a locally weighted regression curve with shaded 95% CIs. The Spearman ρ is reported with 95% bootstrapped CIs (based on 10 000 replicates). The correlation between ICU admissions and number of beds increased between 2006 (Spearman ρ, 0.28 [95% CI, 0.01–0.52]) and 2015 (Spearman ρ, 0.58 [CI, 0.30–0.77]). ICU = intensive care unit.

    Figure 2. Percentage of change in ICU beds and admissions.

    Percentage of change in ICU admissions per 100 000 persons aged ≥65 y vs. percentage of change in total ICU beds per 100 000 persons in each state. Lines represent a locally weighted regression curve with shaded 95% CIs. The Spearman ρ is reported with 95% bootstrapped CIs (based on 10 000 replicates). The percentage changes in ICU beds and admissions between 2006 and 2015 were not correlated (Spearman ρ, 0.03 [95% CI, −0.24 to 0.30]). ICU = intensive care unit.

    Discussion: This nationally representative study shows that ICU admission rates among Medicare fee-for-service beneficiaries have decreased in the past decade. Large, state-level differences in ICU admission rates were partially associated with ICU bed availability, whereas temporal changes in admission rates were not associated with bed growth. This observation is consistent with previously reported heterogeneity in bed growth and occupancy at the state (4) and hospital (5) levels.

    Limitations of this study include the use of Medicare Provider Analysis and Review data, which do not represent populations enrolled in other insurance plans or those without insurance. We also did not adjust for patient-level factors, such as comorbidities, clinician practice patterns, or market competition, which would probably explain some differences in ICU admission rates.

    Policy and population health strategies to promote high-value care for Medicare fee-for-service beneficiaries requiring ICU services probably vary among states. The United States has more ICU beds per capita than many peer nations (2); however, bed availability is not the sole driver of ICU admissions and its effects also vary across states. Institution of federal policies governing critical care workforce training, reimbursements for critical care services, and state-level approvals of certificates of need will thus require more local and granular data.

    Disclaimer: The funding sources had no role in the study.

    Acknowledgment: The authors thank George L. Anesi, MD, MSCE, MBE, and Rachel Kohn, MD, MSCE, from the Palliative and Advanced Illness Research Center and Pulmonary and Critical Care Division at the University of Pennsylvania for their comments in developing this study.

    Grant Support: By National Institutes of Health/National Heart, Lung, and Blood Institute grants T32-HL098054 and K23-HL141639 (Dr. Weissman) and in part by National Institutes of Health/National Institute on Aging grant K24-AG047908 (Dr. Werner and Ms. Yuan).

    Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at

    Reproducible Research Statement:Study protocol and data set: Not available. Statistical code: Available from Dr. Weissman (e-mail, ).

    This article was published at on 16 October 2018.

    Gary E. Weissman, MD, MSHP

    Meeta Prasad Kerlin, MD, MSCE

    Palliative and Advanced Illness Research Center, Perelman School of Medicine, and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania

    Yihao Yuan, MS

    Nicole B. Gabler, PhD, MPH

    Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

    Peter W. Groeneveld, MD, MS

    Rachel M. Werner, MD, PhD

    Leonard Davis Institute of Health Economics and Perelman School of Medicine, University of Pennsylvania, and Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania

    Scott D. Halpern, MD, PhD

    Palliative and Advanced Illness Research Center, Perelman School of Medicine, and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania


    1. Halpern SD. ICU capacity strain and the quality and allocation of critical care. Curr Opin Crit Care. 2011;17:648-57. [PMID: 21986461] doi:10.1097/MCC.0b013e32834c7a53

    2. Wunsch H, Angus DC, Harrison DA, Collange O, Fowler R, Hoste EA, et al. Variation in critical care services across North America and Western Europe. Crit Care Med. 2008;36:2787-93. [PMID: 18766102] doi:10.1097/CCM.0b013e318186aec8

    3. Weissman GE, Hubbard RA, Kohn R, Anesi GL, Manaker S, Kerlin MP, et al. Validation of an administrative definition of ICU admission using revenue center codes. Crit Care Med. 2017;45:e758-62. [PMID: 28441234] doi:10.1097/CCM.0000000000002374

    4. Wallace DJ, Angus DC, Seymour CW, Barnato AE, Kahn JM. Critical care bed growth in the United States. A comparison of regional and national trends. Am J Respir Crit Care Med. 2015;191:410-6. [PMID: 25522054] doi:10.1164/rccm.201409-1746OC

    5. Wallace DJ, Seymour CW, Kahn JM. Hospital-level changes in adult ICU bed supply in the United States. Crit Care Med. 2017;45:e67-76. [PMID: 27661861]


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