Which Half Are You? Almost Half of Pediatric Oncologists and Intensivists Are Burnt Out……

K. Sarah Hoehn, MD, MBe¹

Manjusha Abraham, MD²

John Gaughan, PhD³

Brigham C. Willis, MD⁴

¹Department of Pediatric Critical Care, University of Chicago Comer Children’s Hospital, Chicago IL

²Department of Pediatrics, Section of Critical Care, St. Mary’s Hospital, St Louis MO

³Biostatistics Consulting Center, Temple University School of Medicine, Philadelphia, PA

⁴Division of Cardiovascular Intensive Care, Department of Child Health, University of Arizona College of Medicine – Phoenix and Phoenix Children’s Hospital, Phoenix, AZ

Abstract

Objective: To study the prevalence of burnout, secondary traumatic stress, and wellbeing among pediatric critical care and pediatric hematology and oncology physicians 

Design: Observational cohort study

Setting: Online survey

Patients: Active American Academy of Pediatrics (AAP) members of the section of critical care and the section of hematology and oncology

Interventions: Surveys containing three validated instruments (the Maslach Burnout Inventory, the secondary traumatic stress scale and the Personal Wellbeing Index, as well as questions on demographics and lifestyle) were emailed out via the AAP.

Measurements and Main Results: We had 231 respondents with a response rate of 15.8% among PICU physicians and 26.1% among hematology-oncology physicians. 45.9% of our participants consisted of hematology-oncology physicians and 54.1% of pediatric critical care physicians. The population was a balanced gender mix but was predominantly Caucasian (82% Caucasian and 10% Asian). The overall rate of burnout was 46.6% (47.8% among hematology-oncology physicians and 45.8% among pediatric intensivists). We found significant rates of emotional exhaustion, with 43.0% of respondents scoring high on this subscale.

The prevalence of secondary traumatic stress was 46.8% (42.5% among hematology-oncology physicians and 50.9% among pediatric intensivists). Physicians in practice over 10 to 15 years had significantly higher rates of secondary traumatic stress (p < 0.05). No other demographic or lifestyle variable was significantly associated with an increased risk of burnout or secondary traumatic stress.

Conclusion: Our study reports concerning rates of burnout and secondary traumatic stress among pediatricians in the specialties of Hematology/Oncology and Pediatric Critical Care Medicine. The results raise concern for better screening and prevention for burnout in these high risk specialties. Promoting recognition of early symptoms is crucial, as well as creating a work environment that promotes mental health.

Background

For millennia, physicians have promised to take care of patients to the best of our abilities. In doing so, physicians make personal sacrifices and face challenging situations, including significant administrative burdens of the electronic medical record; all of which may contribute to burnout (1). This led to the AMA supporting a Charter of Physician Well Being, highlighting the importance of building resilience among physicians (2). The topic of physician burnout as one of the leading stories in 2017 (3). Physicians have a have a higher rate of burnout compared to US workers in other fields (4). Burnout has been defined as “a syndrome of emotional exhaustion and cynicism that occurs frequently among individuals who do ‘people-work’ of some kind” (5). Burnout syndrome has 3 key dimensions: emotional exhaustion, depersonalization and lack of personal accomplishment. These problems can affect not only physicians themselves but also patient care. Studies show that burnout is more common among physicians who are 11-20 years in practice (6). A German study suggests that female senior physicians having children are at the greatest risk for burnout (7).  

Along with burnout, physicians may face post-traumatic stress. It has become increasingly more evident that trauma does not only affect the individual(s) directly involved, but also others around them, including healthcare workers. Thus, the concept of secondary traumatic stress has been defined. Secondary traumatic stress (STS) is defined as “the natural, consequent behaviors and emotions resulting from knowledge about a traumatizing event experienced by a significant other. It is the stress resulting from helping or wanting to help a traumatized or suffering person” (8). STS has been studied in a variety of caregiving populations, including social workers, nurses, chaplains, and child life specialists, but there is only limited to no data on STS among pediatric physicians (9-12).

In studying the prevalence of burnout and secondary traumatic stress among physicians, we would be remiss not to also assess the overall wellbeing of these individuals. Wellbeing is defined as “a relative state where one maximizes his or her physical, mental, and social functioning in the context of supportive environments to live a full, satisfying, and productive life”.  The measurement of wellbeing in all Americans is a Healthy People 2020 objective (13).

We used standardized instruments to assess the prevalence of burnout, the prevalence of secondary post-traumatic stress, and the overall wellbeing of high-risk pediatric physicians. We hypothesized that pediatric critical care physicians and pediatric hematology/oncology physicians would have similarly high rates of burnout, STS and adverse effects on overall wellbeing.

Methods

The study was reviewed and approved by the Institutional Review Board of Kansas University Medical Center via expedited review. Four questionnaires (Maslach Burnout Scale, Secondary Traumatic Stress, Personal Wellbeing Index, demographic survey) were emailed to the section of critical care medicine and the section on pediatric hematology and oncology of the American Academy of Pediatrics. Reminders to complete the surveys were sent out at 4 and 6 weeks after the initial email. No identifiable data was recorded.

Maslach Burnout Inventory (MBI)

The Maslach Burnout Inventory (MBI) was developed to study burnout syndrome, and has 3 sub scales focusing on the areas of emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA). It consists of 22 items on a questionnaire that uses a six point Likert scale (Appendix 1). A high degree of burnout is reflected by high scores on the emotional exhaustion and depersonalization scale in addition to low scores on the personal accomplishment scale. The MBI has been shown to have coefficient alpha between 0.70 to 0.80 in 84 different studies that used the MBI to assess burnout, indicating that the MBI has good internal consistency in low stakes testing (14). Since its initial publication in 1980, the MBI has been shown to adequately assess the presence or absence of burnout in a variety of physician groups (15-19). In our study we defined burnout as the presence of at least one of the following: EE ≥ 37 or DP ≥ 13 or PA < 31 (15).

Secondary Traumatic Stress Scale (STSS)

The Secondary Traumatic Stress Scale (STSS) was developed by Bride and colleagues (20) by using the seventeen symptoms of post traumatic stress disorder from the DSM-IV, and has seventeen items that are answered using a five point Likert type scale. It has been found to have an overall coefficient alpha of 0.94. There are three subscales and each subscale has a coefficient alpha as well: intrusion, 0.80; avoidance, 0.87; arousal 0.79.

Personal Wellbeing Index (PWI)

The Personal Wellbeing Index (PWI) scale contains 7 questions, each one addressing a quality of life domain: standard of living, achieving in life, health, relationships, safety, community-connectedness, and future security. In regards to reliability, the Cornbach alpha lies between 0.70 and 0.85 in Australia and overseas and the index has shown good test-retest reliability with an intra-class correlation coefficient of 0.84 (21).

Demographic Survey

The demographic questionnaire is a self-constructed survey with common factors (years in practice, hours of sleep at night, hours of exercise per week, healthy diet, marital status, number of children, religion) that can be associated as a risk versus protective factors for burnout, secondary traumatic stress and overall wellbeing. Each factor had a comment section for qualitative analysis.

Data Analysis

Variables measured on a continuous scale are presented as means with standard deviations. Groups were compared using the Wilcoxon rank sum test and ANOVA on ranks. Categorical measurements are presented as frequencies with percentages. Groups were compared using Fisher’s exact test and chi-square. A value of < 0.05 was considered statistically significant. All analyses were carried out using SAS V9.2 statistical software (SAS Institute, Cary, NC).

Results

Demographics

Our study population consisted of 231 participants, in which hematology/oncology physicians and pediatric critical care physicians were evenly distributed (45.89% vs 54.1%). Initially the study was sent out to 732 members of AAP section of pediatric critical care and 445 members of the AAP section of hematology and oncology. The response rate was 15.8% among PICU physicians and 26.1% among hematology and oncology physicians. We attribute our low response rate to the automated depersonalized email from a website, rather than individual requests to members. Surveys that were started but were determined to be incomplete were excluded.

The population was gender balanced (female 51.8%, male 48.2 %), but predominantly Caucasian. 82.5% identified themselves as Caucasian, 10.8% as Asian, 0.9% as African American and 5.8% as others. With regard to religion more than half identified themselves as Christians (56.1%) followed by 25.3% who chose not to specify their religion. 11.8% identified themselves as Jewish, 3.6% as Hindus and 3.2% as Muslims. Most of our participants (76.8%) were married. 35.1% had 2 children followed by 22.1% who had no children. This study group mostly consisted of physicians who were > 20 years in practice (40.6%). 75.5% sleep 5-7 hours per night and 58.4% exercise 2-3 times per week. Half of this group (53.1%) claimed to consume a healthy diet (Table 1-2).

Table 1. Demographic Characteristics (n=231)

Table 2. Habits (n=231)

Maslach Burnout Inventory

The overall burnout rate was 46.8% (45.8% among pediatric critical care physicians and 47.8% among hematology/oncology physicians) (Table 3).

Table 3: Comparison of burnout, secondary traumatic stress and wellbeing rates between pediatric critical care and hematology oncology physicians

Almost half of the participants scored high (42.9%) on the emotional exhaustion subscale and 20.2% scored high for depersonalization. 50.5% also scored high on the personal accomplishment scale. 52.4% of burned out physicians were female. One third of physicians at risk for burnout had 2 children, but the number of children did not correlate with an increased risk of burnout. No demographic factors were identified as a risk or a protective factor for the development of burnout.

Secondary Traumatic Stress Scale

STS was defined as a total score of > 38. The rate of STS was 46.7% (Table 3). A higher total STSS score was noted for physicians practicing for 10- 15 years compared to those practicing for 5-10 years (p=0.04) with a higher score on the arousal subscale (p=0.03). Physicians who followed a healthy diet had a lower total STS score (p=0.01) and a lower score on all three subscales. The same group also seems to have higher scores on the wellbeing scale (p=0.01).

Personal Wellbeing Index

A Personal Wellbeing Index score of >35 was defined as a positive score, which means that an individual was satisfied with his personal life. The overall rate of satisfaction was 95.3% (Table 3). There was no significant difference for PWI scores for critical care and hematology/oncology physicians. With regard to hours of sleep per night, there was no significant difference in burnout or STS rate. However, physicians who slept >7h had a higher score on the PWI scale compared to those who sleep 3-5h (p=0.008) and 5-7h (p=0.02). Married physicians scored higher on the wellbeing scale compared to single physicians (p=0.04). Neither the number of children nor any other lifestyle or demographic factors were associated with increased wellbeing.

Discussion

Our results demonstrate high rates of burnout and secondary traumatic stress in pediatric critical care and pediatric hematology/oncology physicians. This is consistent with recent studies showing that burn out starts during pediatrics residency (18). Fields et al studied burnout rates among PICU physicians 20 years ago and found a rate of 14%, which is significantly lower than our findings. Garcia et al reported a burnout rate of 50% among general pediatricians and pediatric intensivists (19), in line with our findings.  Burn out is not unique to Americans. Other studies have reported a rate of 41% at high risk for burnout among pediatric critical care physicians in Argentina (22).  Interestingly, this study also found the highest rates among academic pediatricians working in a university setting. Comparing with other specialties, surgeons had similar rates of burnout, ranging from 39-41% (4).

Interestingly our study shows that physicians that are in practice for >20 years had higher scores on the depersonalization subscale. This is in contrast to prior studies that showed that physicians in the middle of their career (11-20 years in practice) are at the greatest risk for burnout (4). Another study by Downey et al assessed burnout among anesthesiologists and came to the conclusion that doctors who are 5-15 years in practice are at the greatest risk for burnout. In our study, physicians who are 10-15 years into their careers had higher secondary traumatic stress scores. Unfortunately, there is not much literature to compare our rates of secondary traumatic stress to and available data is mainly focused on military physicians (22).

We did find that a number of factors can mitigate burnout and STS rates. A healthy diet, sleep and religion positively influenced wellbeing and secondary traumatic stress rates. A subjectively healthy diet was associated with decreased total secondary traumatic stress scores and increased scored on the personal wellbeing scale. Consuming fruits and vegetables is associated with lower incidents of depression and higher rates of happiness and higher life satisfaction (23-25). Along with a healthy diet, more than 7 hours of sleep is also associated with physician wellbeing. It is well known that sleep deprivation is associated with decreased cognitive function, memory and reaction time (26).

Burnout poses a risk for the physician and the patient. High scores on the depersonalization and emotional exhaustion subscale are associated with alcohol abuse or dependence (27). Oreskovich et al. (28) sampled 25,073 surgeons, out of which 15.4% were identified to have an alcohol abuse disorder. Participants who were burned out (odds ratio, 1.25; P = .01) and depressed (odds ratio, 1.48; P < .001) were more likely to have alcohol abuse or dependence. Other studies have identified a correlation between burnout rates (specifically emotional exhaustion) and patient safety risks. Clinicians who scored high on the emotional exhaustion subscale of the MBI had higher standardized mortality ratios (29). A Mayo Clinic study also clearly linked burnout with self-perceived medical errors in both internal medicine residents and surgeons (30). In contrast, a recent study conducted in the adult ICU setting established that there is an increased rate of medical errors by depressed physicians, but burn out did not seem to correlate with an increase rate of medical errors (31). Another prospective cohort study done in three children’s hospitals on pediatric residents have had similar results. (32). In our study we did not measure depression or assess for medical errors related with physician burnout. More studies are needed in the future to elicit if burnout leads to an increase rate of medical errors and the potential risks for the patients.

One important limitation of this study is that it was sent to members of the American Academy of Pediatrics, where 40.63% of the physicians are >20 years in practice. This could have skewed the outcomes. One limitation in our study may be that respondents to our survey could be those who are more likely to suffer from burnout and more likely to want to report their issues, or conversely, those most severely affected may have chosen not to participate. We also did not separately analyze burnout and STS against each other, and we presumed that the similar rates were in the same respondents, but that may not be accurate.

Conclusion

The rates of burnout and secondary traumatic stress are high in both pediatric critical care physicians and pediatric hematologist / oncologists. It may be that lifestyle factors, such as a healthy diet, sleep and exercise may serve as protective factors and increase overall wellbeing. Further studies need to be done to assess burnout, secondary traumatic stress rates among other pediatric subspecialties and to analyze proper coping mechanisms.

References

1. Schwenk TL. Physician well-being and the regenerative power of caring. JAMA. 2018 Apr 17;319(15):1543-4. [CrossRef] [PubMed]
2. AMA strongly supports the charter on physician well-being. March 29, 2018. Available at: www.ama-assn.org/ama-strongly-supports-charter-physician-well-being (accessed 6/14/19).
3. Berg S. Physician well-being again a burning topic in 2017. AMA. December 20, 2017. Available at: https://www.ama-assn.org/press-center/press-releases/ama-strongly-supports-charter-physician-well-being (accessed 6/14/19).
4. Shanafelt TD, Boone S, Tan L, Dyrbye LN, Sotile W, Satele D, West CP, Sloan J, Oreskovich MR. Burnout and satisfaction with work-life balance among US physicians relative to the general US population. Arch Intern Med. 2012 Oct 8;172(18):1377-85. [CrossRef] [PubMed]
5. Maslach C, Jackson S. (1981). The Measurement of Experienced Burnout. Journal of Occupational Behaviour. 1981;2(2): 99-113. [CrossRef] [PubMed]
6. Dyrbye LN, Varkey P, Boone SL, Satele DV, Sloan JA, Shanafelt TD. Physician satisfaction and burnout at different career stages. Mayo Clin Proc. 2013 Dec;88(12):1358-67. [CrossRef] [PubMed]
7. Richter A, Kostova P, Harth V, Wegner R. Children, care, career - a cross-sectional study on the risk of burnout among German hospital physicians at different career stages. J Occup Med Toxicol. 2014 Dec 3;9(1):41. [CrossRef] [PubMed]
8. Figley CR. Compassion Fatigue: toward a new understanding of the cost of caring. [book auth.] Secondary traumatic stress: self care issues for clinicians, researchers, and educators. Lutherville MD: Sidaran 1999.
9. Meadors P, Lamson A, Swanson M, White M, Sira N. Secondary traumatization in pediatric healthcare providers: compassion fatigue, burnout, and secondary traumatic stress. Omega (Westport). 2009-2010;60(2):103-28. [CrossRef] [PubMed]
10. Badger K, Royse D, Craig C. Hospital social workers and indirect trauma exposure: an exploratory study of contributing factors. Health Soc Work. 2008 Feb;33(1):63-71. [CrossRef] [PubMed]
11. Benuto LT, Yang Y, Ahrendt A, Cummings C. The secondary traumatic stress scale: Confirmatory factor analyses with a national sample of mental health social workers. Journal of Interpersonal Violence. March 11, 2018. [CrossRef] [PubMed]
12. Dominguez-Gomez E, Rutledge DN. Prevalence of secondary traumatic stress among emergency nurses.J Emerg Nurs. 2009 Jun;35(3):199-204. [CrossRef] [PubMed]
13. US Department of Health and Human Services, Office of Disease Prevention and Health Promotion. Healthy People 2020. Washington, D.C. Federal Government of the United States, 2010. Available at: https://www.healthypeople.gov/ (accessed 6/14/19).
14. Wheeler DL, Vassar M, Worley JA, Barnes LLB. A reliability generalization meta analysis of coefficient alpha for the Maslach burnout inventory. Educational and Psychological Measurement. 2011;71:231-44. [CrossRef]
15. Rafferty JP, Lemkau JP, Purdy RR, Rudisill JR. Validity of the Maslach Burnout Inventory for family practice physicians. J Clin Psychol. 1986 May;42(3):488-92. [CrossRef] [PubMed]
16. Chopra SS, Sotile WM, Sotile MO. STUDENTJAMA. Physician burnout. JAMA. 2004 Feb 4;291(5):633.[CrossRef] [PubMed]
17. Shanafelt TD, Bradley KA, Wipf JE, Back AL. Burnout and self-reported patient care in an internal medicine residency program. Ann Intern Med. 2002 Mar 5;136(5):358-67. [PubMed]
18. Mahan JD. Burnout in pediatric residents and physicians: a call to action. Pediatrics. 2017 Mar;139(3). pii: e20164233. [CrossRef] [PubMed]
19. Garcia TT, Garcia PC, Molon ME, Piva JP, Tasker RC, Branco RG, Ferreira PE.Prevalence of burnout in pediatric intensivists: an observational comparison with general pediatricians. Pediatr Crit Care Med. 2014 Oct;15(8):e347-53. [CrossRef] [PubMed]
20. Bride BE, Robinson MM, Yegidis B, Figley CR. Development and validation of the secondary traumatic stress scale. Research on Social Work Practice. 2004; 14(1): 27-35. [PubMed]
21. Personal Wellbeing Index- Adult, 5th Edition, 2013. International Wellbeing Group, Robert A Cummins, PhD, FAPsS, Deakin University, Australia
22. Galván ME, Vassallo JC, Rodríguez SP, et al. Professional burnout in pediatric intensive care units in Argentina. Arch Argent Pediatr. 2012 Dec;110(6):466-73. [CrossRef] [PubMed]
23. Kintzle S, Yarvis JS, Bride BE. Secondary traumatic stress in military primary and mental health care providers. Mil Med. 2013 Dec;178(12):1310-5. [CrossRef] [PubMed]
24. Conner TS, Brookie KL, Richardson AC, Polak MA. On carrots and curiosity: eating fruit and vegetables is associated with greater flourishing in daily life. Br J Health Psychol. 2015 May;20(2):413-27. [CrossRef] [PubMed]
25. White BA, Horwath CC, Conner TS. Many apples a day keep the blues away--daily experiences of negative and positive affect and food consumption in young adults. Br J Health Psychol. 2013 Nov;18(4):782-98. [CrossRef] [PubMed]
26. Girbe F, Ramassamy C, Piton C, Costentin J. Ascorbic acid increases synaptosomal potassium-induced dopamine release. Neuroreport. 1994 May 9;5(9):1027-9. [CrossRef] [PubMed]
27. Jarral OA, Baig K, Shetty K, Athanasiou T. Sleep deprivation leads to burnout and cardiothoracic surgeons have to deal with its consequences. Int J Cardiol. 2015 Jan 20;179:70-2. [CrossRef] [PubMed]
28. Oreskovich MR, Kaups KL, Balch CM, Hanks JB, Satele D, Sloan J, Meredith C, Buhl A, Dyrbye LN, Shanafelt TD. Prevalence of alcohol use disorders among American surgeons. Arch Surg. 2012 Feb;147(2):168-74. [CrossRef] [PubMed]
29. Welp A, Meier LL, Manser T. Emotional exhaustion and workload predict clinician-rated and objective patient safety. Front Psychol. 2015 Jan 22;5:1573. [CrossRef] [PubMed]
30. West CP, Tan AD, Habermann TM, Sloan JA, Shanafelt TD. Association of resident fatigue and distress with perceived medical errors. JAMA. 2009 Sep 23;302(12):1294-300. [CrossRef] [PubMed]
31. Garrouste-Orgeas M, Perrin M, Soufir L, et al. The Iatroref study: medical errors are associated with symptoms of depression in ICU staff but not burnout or safety culture. Intensive Care Med. 2015 Feb;41(2):273-84. [CrossRef] [PubMed]
32. Fahrenkopf AM, Sectish TC, Barger LK, et al. Rates of medication errors among depressed and burnt out residents: prospective cohort study. BMJ. 2008 Mar 1;336(7642):488-91. [CrossRef] [PubMed]

 Cite as: Hoehn KS, Abraham M, Gaughan J, Willis BC. Which half are you? Almost half of pediatric oncologists and intensivists are burnt out…… Southwest J Pulm Crit Care. 2019;18(6):167-76. doi: https://doi.org/10.13175/swjpcc029-19 PDF 

Increased Incidence of Eosinophilia in Severe H1N1 Pneumonia during 2015 Influenza Season

Benjamin Deaton MD

Nicholas Villalobos MD

Andrea Mytinger DO

Michel Boivin MD

Department of Internal Medicine

University of New Mexico School of Medicine

Albuquerque, NM USA

Abstract

Background: A portion of patients with influenza develop a severe, life t-threatening illness requiring intensive care. We observed a significant number of critically ill influenza patients with eosinophilia during the 2015 influenza season in New Mexico.

Methods: Patients were identified sequentially by reviewing disposition records of all patients admitted to the University of New Mexico Hospital medical intensive care unit between October 2015 and May 2016 for a diagnosis of influenza.

Results: Eleven patients were identified who developed respiratory failure from influenza. Average age was 43.7 + 11.3 (SD) with an average SAPS-2 score of 52.0 + 13.9 (SD) on admission. All 11 were found to have H1N1 influenza. All 11 required mechanical ventilation vasopressor support. Ten patients survived. Notably, 6 (54.5%) developed peripheral eosinophilia (>300/μL) during their hospitalization and all but one of these did not have peripheral eosinophilia at the time of admission. Bronchoalveolar lavage was performed in 5 patients (45.5%) and none were consistent with eosinophilic pneumonia. Further data analysis revealed exploration revealed no significant differences in multiple parameters and no clear cut cause of drug-induced eosinophilia was identified.

Conclusion: During the 2015 influenza season in New Mexico, a disproportionate number of patients with H1N1 influenza and respiratory failure developed peripheral eosinophilia. Type 2 errors could have occurred due to low sample size. Given the unusual frequency of peripheral eosinophilia further studies regarding the association of influenza A and peripheral eosinophilia is warranted.

Introduction

Influenza pneumonia remains a cause of significant morbidity and mortality (1). The re-emergence of H1N1 influenza in 2009 was associated with particularly severe respiratory illness, acute respiratory distress syndrome (ARDS) and mortality (2). The ARDS associated with H1N1 influenza appeared to disproportionately affect younger individuals, compared to other strains of influenza A (2). During the 2015 influenza season H1N1 circulated relatively late in the southwestern United States (3). Intensivists caring for patients with severe H1N1 pneumonia at the University of New Mexico hospital noticed a series of cases associated with significant peripheral eosinophilia. Eosinophilia with influenza or its treatments has rarely been described (4). We therefore sought to examine all cases of severe influenza pneumonia during the 2015 influenza season for the prevalence of peripheral eosinophilia and to assess for potential associations.

Methods

This study was reviewed and approved by the Institutional Review Board of the University of New Mexico Health Sciences Center. Patients from the University of New Mexico Hospital (UNMH) adult Medical Intensive Care Unit (MICU) admitted between October 2015 through May 2016 were retrospectively screened for inclusion. Inclusion criteria included a diagnosis of influenza (using a PCR based assay of nasal swab), admission to the UNMH MICU and age ≥ 18 years. Exclusion criteria included patients admitted to the MICU where influenza did not lead to significant respiratory failure.

In this retrospective cohort chart review, data was collected for demographics, clinical parameters at presentation and throughout their hospital course, and interventions received. Patients were assessed for the presence of eosinophilia at any point during their hospital course. Eosinophilia was defined as a serum eosinophil count that exceeded the upper limit of normal on a complete blood count (0.3x10³ cells/microliter). Values are reported with their standard deviation. Statistical analysis was performed using Stata 14 for Mac. The data was explored using two-sided t-tests, Fisher’s exact and Chi-squared tests between the 2 groups with and without eosinophilia. The paper was partially presented in poster form at the 2017 American Thoracic Society International Congress in Washington, DC (5).

Results

Thirteen patients with influenza were identified. Two patients were excluded from further analysis as they did not meet the criteria of having respiratory failure, the remaining eleven were included in this study. The average age of patients in the study was 43.7 ±11.3 years with an average SAPS-2 score of 52.0 ± 13.9 on admission. All eleven patients in the study admitted with severe influenza A leading to respiratory failure during the 2015-2016 influenza season were found to be infected by the H1N1 strain of influenza. See Table 1 for further descriptors of the cohort.

Table 1. Baseline and treatment characteristics by group.

The peak eosinophil count of the group with normal eosinophil count was 0.1(+0.1) X10³ cells/µl compared to 1.9 (+ 2.1) X10³ cells/µl in the group with significant peripheral eosinophilia (p=0.06). The range of eosinophilia in the group with normal eosinophil count was 0.0-0.3 X10³ cells/µl, and 0.5-4.8 X10³ cells/µl in the group with eosinophilia. The group with normal eosinophil count reached a “peak” count after an average of 4.6 days, and the group with an elevated eosinophil count after 17.1 days (p<0.02).None of the patients who underwent bronchoscopy had a significant elevation in the bronchoalveolar lavage eosinophil count.

Discussion

During the 2015-2016 influenza season in New Mexico, critically ill patients at UNM hospital admitted with influenza pneumonia were infected with the H1N1 subtype. Over 50 percent of these patients developed peripheral eosinophilia at some point of their hospital course. Among those who underwent bronchoscopy, significant alveolar eosinophilia was not observed, suggesting against a pulmonary cause of eosinophilia, such as acute or chronic eosinophilic pneumonia. All patients were treated with oseltamivir, so an association with this treatment could not be determined. No demographic differences were noted between patients who vashad peripheral eosinophilia and those that did not. The patients with significant peripheral eosinophilia trended to have a longer ICU and hospital length of stay (LOS) but this did not reach statistical significance in this small cohort.

Type 2 errors (failure to detect a true difference between groups due to small numbers of subjects) could have occurred due to low sample size while exploring etiologies. Potential etiologies that could have explained the observed eosinophilia included drug effect, possibly due to oseltamivir, antibiotics, diuretics or other medications. A review of the literature reveals case reports of associations between eosinophilia and influenza vaccine (6,7). Acute eosinophilic pneumonia has also been associated with H1N1 infection, but eosinophilia was not demonstrated on broncho-alveolar lavage in our series (8.9). Potentially this could have been a reaction to epitopes of this particular strain of H1N1 influenza. However, there have yet to be reports of eosinophilia during the 2015-2016 influenza season in the literature. Perhaps local factors could have contributed to an increased incidence of significant peripheral eosinophilia. Anecdotally, the authors do not however recall an increased incidence of eosinophilia in patients admitted for diagnoses other than H1N1. Patients were screened for other causes of viral pneumonia, and there was no clear co-infection that was associated with influenza associated eosinophilia. It was also noted the time to peak eosinophil count was much later in the elevated eosinophil group, and in most it took 14 days for the count to peak. This suggests the stimulus for the eosinophilia was ongoing for considerable time during the admission.

In conclusion, we describe an unusually high incidence of peripheral eosinophilia in patients with severe H1N1 influenza during the 2015 flu season. This eosinophilia was not associated with alveolar eosinophilia. Further observation for the recurrence of this association of H1N1 influenza A and peripheral eosinophilia is warranted during future influenza seasons.

References

1. Rotrosen ET, Neuzil KM, Influenza: a global perspective. Pediatr Clin North Am. 2017;64:911-36. [CrossRef] [PubMed]
2. Davlin SL, Blanton L, Kniss K, et al. Influenza Activity - United States, 2015-16 Season and Composition of the 2016-17 Influenza Vaccine.MMWR Morb Mortal Wkly Rep. 2016 Jun 10;65(22):567-75. [CrossRef] [PubMed]
3. Uyeki TM. Influenza. Ann Intern Med. 2017 Sep 5;167(5):ITC33-ITC48. [CrossRef] [PubMed]
4. Deaton, BR., Mytinger, AK, Ahmed, S, et al. Peripheral eosinophilia associated with 2016 H1N1 influenza. Am J Resp Crit Care. 2017;195:A5787 [Abstract],
5. Hayashi R, Shimomura N, Hosojima M, et al. A case of non-episodic angioedema with eosinophilia induced by influenza vaccine. Eur J Dermatol. 2017;27:554-5. [CrossRef] [PubMed]
6. Solak B, Dikicier BS, Kara RO, Erdem T. DRESS syndrome potentially induced by allopurinol and triggered by influenza vaccine. BMJ Case Rep. 2016 Mar 30;2016. [CrossRef] [PubMed]
7. Larrañaga JM, Marcos PJ, Pombo F, Otero-González I. Acute eosinophilic pneumonia as a complication of influenza A (H1N1) pulmonary infection. Sarcoidosis Vasc Diffuse Lung Dis. 2016 Mar 29;33(1):95-7. [PubMed]
8. Jeon EJ, Kim KH, Min KH. Acute eosinophilic pneumonia associated with 2009 influenza A (H1N1). Thorax. 2010;65:268-70. [CrossRef] [PubMed]

Cite as: Deaton B, Villalobos N, Mytinger A, Boivin M. Increased incidence of eosinophilia in severe H1N1 pneumonia during 2015 influenza season. Southwest J Pulm Crit Care. 2018;16(3):146-9. doi: https://doi.org/10.13175/swjpcc021-18 PDF 

Telemedicine Using Stationary Hard-Wire Audiovisual Equipment or Robotic Systems in Critical Care: A Brief Review

Nidhi S. Nikhanj, MD^1,2

Robert A. Raschke, MD^1,2

Robert Groves, MD^1,2

Rodrigo Cavallazzi, MD³

Ken S. Ramos, MD¹

¹Arizona College of Medicine-Phoenix

Phoenix, AZ USA

²Banner University Medical Center-Phoenix

Phoenix, AZ USA

³University of Louisville School of Medicine

Louisville, KY USA

A shortage of critical care physicians in the United States has been widely recognized and reported (1). Most intensive care units (ICUs) do no not have a formally-trained intensivist in their staff despite compelling evidence that high-intensity intensivist staffing leads to better patient outcomes (1,2). Critical care telemedicine is one potential solution that has expanded rapidly since its inception in 2000 (3). In its simplest form, telemedicine leverages audiovisual technology and the electronic medical record to provide remote two-way communication between a physician and a patient. Current telemedicine models differ by the type of hardware facilitating remote audiovisual interaction, the location of the provider, and the type of patient-care service provided. We collectively have experience with several of these models and feel that future telemedicine programs will likely integrate the most advantageous aspects of each with an increasing role for telemedicine robotics.

The dominant current model for providing critical care telemedicine in large healthcare systems utilizes stationary hard-wired audiovisual equipment linking each ICU room to a centralized control location (4). Typically, this control center provides surveillance of a large number of patients using computerized decision support software linked to the EMR – a single physician can cover approximately 100 patients with the appropriate support infrastructure. This model also provides the ability to remotely “round” on ICU patients and to quickly respond to questions posed by nursing or medical emergencies across a broad geographic range. This approach requires a high up-front capital cost approximated at 50-100K per hospital bed covered (5).

Data supporting the benefit of this model of ICU telemedicine has been mixed, but several considerations are important in appraising the literature. A double-blinded RCT for ICU telemedicine intervention is not feasible. Heterogeneity in clinical workflows and staffing models across the country should be considered when assessing the internal validity and generalizability of published studies. For instance, Thomas and colleagues concluded that a telemedicine ICU service resulted in no overall improvement in mortality or length of stay (LOS) (6), but the tele-intensivists in the study were limited by only being allowed to intervene in the care of less than a third of the study patients. Nassar and colleagues published a negative study in a healthcare system in which resident and attending physicians were already available in-house for overnight patient care (7). Likely, the potential benefit of a telemedicine program can be optimized in a clinical setting in which other physicians are not physically available at the locality 24/7 and telemedicine intensivists are allowed to appropriately intervene when indicated.

Despite these difficulties, there is a growing body of evidence that suggests a centralized telemedicine ICU model is effective in a number of areas including: improvements in compliance with evidence based practices (8, 9), increased job satisfaction of ICU nurses (10) and reduction in the cost of care of the sickest patients in the institutional setting (11). Other studies suggest that a telemedicine platform can reduce mortality and LOS by allowing for earlier intensivist involvement, promoting adherence to best practices, shortening alarm response times and improving access to ICU performance data that can be used to drive continuous quality improvement (12,13).

Commercially available telemedicine robots are mobile units equipped with a digital camera, microphone and monitor screen that provides two-way audiovisual communications with the control center via a wireless internet connection (14). Telemedicine robots can be operated with much lower initial capital costs - for instance, an ICU group at a large acute care hospital might provide coverage at a rural healthcare setting using a single robot (15). Such a system can be used for daily rounding or for reactive consultation. Like hard-wired systems, telemedicine robots have been shown to be well accepted by providers (16) and patients (17), and their use has been associated with reduced ICU length-of-stay and decreased delay in response to clinical events by the physician (18).

Telemedicine robotic systems have several disadvantages – they do not provide large-scale EMR surveillance leveraging computerized decision support logic and they are significantly less efficient than hard-wired systems for high-volume patient care since they have to physically relocate from patient room to patient room.  However, unique capabilities of telemedicine robots are being developed that cannot be duplicated by hard-wired systems. Telemedicine robots can be equipped with a digital stethoscope (19). They can perform physical examination elements that require tactile communication – such as the determination of the Glasgow coma scale (20). A robotic arm can be used to remotely perform point-of-care ultrasonography. This has been successfully operationalized for cardiac, abdomino-pelvic, and vascular indications (21,22). Telemedicine robots have been developed that can place peripheral or central venous catheters (23). The development of surgical robots that incorporate tomographic capability and that can perform battlefield stabilization procedures in either autonomous or teleoperative modes (24) provide a glimpse of the potential for telemedicine robots in the ICU.

Although healthcare systems currently implementing telemedicine services will likely choose either a hard-wired or a robotic model – largely based on cost and the volume of required services - we believe the optimal telemedicine system of the future will and should incorporate both technologies. Real-time data acquisition coupled with ready access to timely interventions constitute the basis for faster deployment of precision health care strategies in the ICU setting.

References

1. Kelley MA, Angus D, Chalfin DB, Crandall ED, et al. The critical care crisis in the United States: A report from the profession. Chest. 2004;125:1514-7. [CrossRef] [PubMed]
2. Pronovost PJ, Angus DC, Dorman T, Robinson KA, et al. Physician staffing patterns and clinical outcomes in critically ill patients. JAMA. 2002;288:2151-62. [CrossRef] [PubMed]
3. Rosenfeld BA, Dorman T, Breslow MJ, et al. Intensive care unit telemedicine: alternate paradigm for providing continuous intensivist care. Crit Care Med. 2000;28:3925-31. [CrossRef] [PubMed]
4. Kahn JM, Cicero BD, Wallace DJ, Iwashyna TJ. Adoption of intensive care unit telemedicine in the United States. Crit Care Med. 2014;42:362-8. [CrossRef] [PubMed]
5. Kumar G, Falk DM, Bonello RS, et al. The costs of critical care telemedicine programs: A systematic review and analysis. Chest. 2013;143:19-29. [CrossRef] [PubMed]
6. Thomas EJ, Lucke JF, Wueste L. Association of telemedicine for remote monitoring of intensive care patients weith mortality, complications and length of stay. JAMA. 2009;302:2671-78. [CrossRef] [PubMed]
7. Nassar BS, Vaughan MS, Jiang L, Reisinger HS, et al. Impact of an intensive care unit telemedicine program on patient outcomes in an integrated health care system. JAMA Intern Med. 2014;174:1160-7. [CrossRef] [PubMed]
8. Ventataraman R, Ramakrishnan N. Outcomes related to telemedicine in the intensive care Unit. Crit Care Clinics 2015;31:225-37. [CrossRef] [PubMed]
9. Youn BA. ICU process improvement using telemedicine to enhance compliance and documentation for the ventilator bundle. Chest. 2006;130:(meeting abstracts) 226S-c.
10. Hoonakker PL, Carayon P, McGuire K, et al. Motivation and job satisfaction of tele-ICU nurses. J Crit Care. 2013;28:890-901. [CrossRef] [PubMed]
11. Franzini L, Sail KR, Thomas EJ, et al. Costs and cost-effectiveness of a telemedicine intensive care unit program in six intensive care units in a large health care system. J Crit Care. 2011;26:329e1-6. [CrossRef] [PubMed]
12. Lilly CM, Cody S, Zhao H. Hospital mortality, length of stay and preventable complications among critically ill patients before and after tele-ICU reengineering of critical care processes. JAMA. 2011;305:2175-83. [CrossRef] [PubMed]
13. Lilly CM, Zubrow MT, Kempner KM, Reynolds H, et al. Critical Care telemedicine: Evolution and state of the art. Crit Care Med. 2014;42:2429-36. [CrossRef] [PubMed]
14. Chung KK, Grathwohl KW, Poropatich RK, Wolf SE, et al. Robotic telepresence: Past present and future. Journal of Cardiothoracic and Vascular Anesthesia. 2007;21:593-6. [CrossRef] [PubMed]
15. Murray C, Ortiz E, Kubin C. Application of a robot for critical care rounding in small rural hospitals. Crit Care Nurs Clin North Am. 2014;26:477-85. [CrossRef] [PubMed]
16. Reynolds EM, Grujovski A, Wright T, Foster M, Reynolds HN. Utilization of robotic remote presence technology within North American intensive care units. Telemedicine and e-health. 2012;18:507-15. [CrossRef] [PubMed]
17. Sucher JF, Todd SR, Jones SL, Throckmorton T, et al. Robotic telepresence: A helpful adjunct that is viewed favorably by critically ill surgical patients. Am J Surg. 2011;202:843-7. [CrossRef] [PubMed]
18. Vespa PM, Miller C, Hu X, Nenov V, et al. Intensive care unit robotic telepresence facilitates rapid physician response to unstable patients and decreased cost in neurointensive care. Surgical Neurology. 2007;67:331-7. [CrossRef] [PubMed]
19. Lakhe A, Sodhi I, Warrier J, Sinha V. Development of digital stethoscope for telemedicine. J Med Eng Technol. 2016;40:20-4. [CrossRef] [PubMed]
20. Adcock AK, Kosiorek H, Parich P, Chauncey A, Wu Q, Demaerschalk BM. Reliability of robotic telemedicine for assessing critically ill patients with the full outline of unresponsiveness score and Glasgow coma scale. Telemed J E Health. 2017 Jan 13. [CrossRef] [PubMed]
21. Avgousti S, Panayides AS, Jossif AP, Christoforou EG, et al. Cardiac ultrasonography over 4G wireless networks using a tele-operated robot. Healthc Technol Lett. 2016;3:212-7. [CrossRef] [PubMed]
22. Georgescu M, Sacccomandi A, Baudron B, Arbeille PL. Remote sonography in routine clinical practice between two isolated medical centers and the university hospital using a robotic arm: A 1-year study. Telemed J E Health. 2016;22:276-81. [CrossRef] [PubMed]
23. Kobayashi Y, Hong J, Hamano R, Okada K, Fujie MG, Hashizume M. Development of a needle insertion manipulator for central venous catheterization. Int J Med Robot. 2012;8(1):34–44. [CrossRef] [PubMed]
24. Garcia P, Rosen J, Kapoor C, Noakes M, et al. Trauma Pod: a semi-automated telerobotic surgical system. Int J Med Robot. 2009;5:136-46. [CrossRef] [PubMed]

Cite as: Nikhanj NS, Raschke RA, Groves R, Cavallazzi R, Ramos KS. Telemedicine using stationary hard-wire audiovisual equipment or robotic systems in critical care: a brief review. Southwest J Pulm Crit Care. 2017;15(1):50-3. doi: https://doi.org/10.13175/swjpcc087-17 PDF

Organ Failure in Acute Pancreatitis and Its Impact on Outcome in Critical Care

Namrata Maheshwari, MD, IDCCM

Arun Kumar, MD 

Zafar A Iqbal, MD

Amit K Mandal, DNB,DTCD

Abhishek Vyas, MBBS

Jai D Wig, MS

Department of Critical Care Medicine and Pulmonology

Fortis Hospital 

Mohali, Punjab, 160062

India 

Abstract

The most important determinant of mortality in acute pancreatitis is organ failure (OF). The aim of this prospective observational study was to determine the incidence of organ failure in acute pancreatitis and its relation with the extent of necrosis and outcome. Sixty-one patients were divided into 3 groups: no organ failure (NOF), transient organ failure (< 48 hrs) (TOF) or persistent organ failure (> 48 hrs) (POF). Of 61 patients, 30 patients had no organ failure (49.1%), while 11 patients (18%) had TOF and 20 patients (32.7%) had POF. The mean age was 46.5 yrs with male predominance. Pulmonary and renal failures were the most common (32%), followed by CVS (cardiovascular system), coagulation system and CNS (central nervous system). Fourteen (46.4%) patients had one or two OF, 17 (56.6%) had more than two OF. There were no deaths in patients with up to two organ failures but a 70% (7) death rate in those with three organ involvement, 80% (4) with four and 100% with five OF. The percentage of pancreatic necrosis was evaluated for its relationship with organ failure. In the NOF group 19 (63.3%) patients had no necrosis, as compared to 11 patients with necrosis in TOF and POF groups (35.4%). Out of 61 patients, 13 patients died. All 13 patients who expired belonged to the POF group (p <.001). Early persisting and deteriorating organ failure had the worst outcomes. There was an increase in mortality with an increasing number of organs involved. The extent of necrosis was directly related with incidence of organ failure.

Introduction

Acute pancreatitis (AP) is characterized by a variable clinical course varying from a mild self-limited disease (80-90%) to a clinically severe acute pancreatitis (SAP) in 10-20% (1-4). Despite advances in knowledge and treatment of AP, the identification of patients with clinically severe disease on admission remains difficult (1) and the mortality in several series continues to be around 20% (2,5). 

The factors responsible for high mortality in patients with SAP are organ failure (OF) and pancreatic necrosis (6,7). The reported incidence of OF in SAP varies from 28-76 % (5,8,9). The occurrence of organ dysfunction and progressive organ failure has a major impact on outcome. Many patients who succumb to AP within the first two weeks of disease onset do so from overwhelming multiorgan failure (10,11). Other studies have also reported that prognosis deteriorated with an increase in number of organs involved (12,13). Banks and Freeman (14) studied the correlation between mortality and organ failure in patients with acute pancreatitis and documented a median mortality of 3% in patients with single organ failure and 47% in patients with multisystem organ failure. Another study documented that the overall mortality (47.8%) correlated with the number of organs failing (6). The definition of multiorgan failure is broad and encompasses transient to persistent or severe multiorgan failure that requires critical care support (15). Patients with persistent organ failure have a higher mortality as compared to patients where organ failure resolves (16). Johnson and Hial (17) showed that patients with OF that resolved within 48 hours(transient) have a low risk of complications and death in comparison to patients who have persistent organ failure(OF persisting for 3 or more days) and have a greater than one in three risk of fatal outcome. Information regarding the prediction of persistent organ failure in patients with acute pancreatitis is not available (18).

One of the factors linked to the development of OF is the extent of pancreatic necrosis. Some workers have found a correlation between the extent of necrosis and OF (19). The question of the relationship between infected necrosis and OF remains unsettled. There is no consistency in the literature on whether organ failure or infected necrosis is the main determinant of severity in acute pancreatitis. The aim of study was to study the occurrence of organ failure in acute pancreatitis and determine the influence of organ failure on mortality in patients with acute pancreatitis.

Materials and Methods

This study was a prospective study under taken during 18 months (December 2011 to May 2013) in the Departments of Gastroenterology, General Surgery and Medical Intensive Care Unit in Fortis Hospital, Mohali, Punjab, a 260 bedded multispecialty tertiary care hospital in Northern India.

The study sample included all consecutive patients diagnosed with acute pancreatitis referred to Gastroenterology or General surgery units fulfilling the inclusion and exclusion criteria. All the patients were assessed for demographic profile and detailed symptom profile. After a detailed clinical examination relevant investigations were repeated as and when required. Patients were monitored for the presence and severity of organ failure every day during the first week, subsequent local complications, subsequent episodes of sepsis, and death or other outcomes during the same hospital admission.

Organ failure was defined as per modified multiple organ failure score (MMOFS). Transient organ failure was defined as organ failure present for less than 48 hours, and persistent organ failure was recorded when organ failure was present for more than 48 hours, where day 0 was the day of entry to the study and day one started at 8.00am on the day after entry. The course in hospital and final outcome was recorded. Cross tabulations were made with outcome, in particular with mortality.

Statistical Analysis. The data are presented as mean ± SD or median and interquartile range, as appropriate. The Mann- Whitney U-test was used for statistical analysis of skewed continuous variables and ordered categorical variables. For normally distributed data The t-test was applied. Pearson χ2 test or Fisher’s exact test was used for analysis of categorical variables with two categories. A p value of <0.05 was considered to indicate statistical significance. All calculations were performed using SPSS® version 15 (Statistical Packages for the Social Sciences, Chicago, IL).

Results

The study was comprised of 61 patients who met the inclusion criteria with diagnosis of acute pancreatitis. The study group was further divided as per organ failure into three groups:

• No organ failure (NOF)
• Transient organ failure ( < 48 hrs) (TOF)
• Persistent organ failure ( > 48 hrs) (POF)

Demographic Distribution. The mean age of the patients was 46.5 years. The majority of patients were in the age group of 30-50 years. In this study the youngest patient was 17 years old and oldest was 87 years old (Figure 1).  

Figure 1. Age distribution with increased number of organs involvement.

The male to female ratio was found to be 2.4:1 (Figure 2).

Figure 2. Sex distribution.

Male predominance was found in all groups (53.3 %, 81.8%, and 90 % in the no organ, transient and persistent organ failure group respectively).

Comorbid Conditions. A majority of the patients (38) in our study group had no associated comorbid conditions  while 23 patients (37.8%) had a previous comorbid condition. Hypertension was the most common comorbid condition, seen in almost 31 % of the patients at the time of admission. Type 2 diabetes was the second most common condition noted in 24.6%, followed by hypothyroidism (4.9%), asthma, depression, cardiomyopathy and Guillain-Barré syndrome in 1.6% each (Table 1).  

Table 1. Comorbid conditions associated in our study group.

 

We could not find any association between co morbidities and mortality as 9 (62.9%) deaths occurred in the no comorbidity group as compared to 4 (30.8%) deaths in the co morbidities group (p=0.880).

Etiology. The most common etiologies of pancreatitis in our study group were alcohol and gall stones (n=24, 39% each) (Table 2).

Table 2. Etiology of acute pancreatitis.

Other causes were idiopathic (n=10, 17%), hypertriglyceridemia (n=2, 3%) and pancreatic divisum (n=1, 2%).

Percentage of Necrosis and Organ Failure. The percentage of necrosis on radiological imaging (in 46 patients) was evaluated for its relationship with organ failure. In the NOF group 19 (63.3%) patients had no necrosis (0%), 4 (13.3%) patients had <30% necrosis, 1 (3.3%) had 30-50% and 4 (13.3%) had >50% necrosis (Figure 3).

Figure 3. Relation between organ failure and pancreatic necrosis.

In the TOF group, 4 (36.4%) patients revealed no necrosis on contrast-enhanced computerized tomography (CECT) of the abdomen, <30% necrosis in 2 (18.2%) patients, 30-50% necrosis in 3 (27.3%) and >50% in 1 (9.1%) patient (Figure 3).

In POF group no necrosis was detected in 3 (15%) patients, <30 % in 2 (10%), 30-50% in 1 (5%) and >50% in 2 (10%) patients. The relationship between the amount of necrosis was directly related with incidence of organ failure and this correlation was found to be statistically significant (Figure 3).

MMOFS and Mortality. We divided our study in 3 groups, no organ failure, transient (<48 hrs) and persistent (>48 hrs) organ failure to understand the nature and dynamics of organ failure. Groups were further divided in early onset (<7days), late onset (>7days). Organ failure was calculated by the Modified multiorgan failure score (MMOFS). Daily MMOFS was calculated in all patients up to 7 days. MMOFS difference was calculated by MMOFS 7 (MMOFS at day 7) – MMOFS 1 (at the time of admission). On the basis of MMOFS difference groups were further divided into same (if difference was 0), improving (if deference was negative value), or deteriorating (if deference was a positive value) groups (Figure 4).

Figure 4. Comparison of outcome with MMOFS difference.

MMOFS difference was found to be highly significantly (ANOVA, p<0.001 each) correlated with organ failures and outcome. In our study no deaths occurred in the transient OF groups (early transient, late transient and transient deteriorating). We attributed this to the dynamics that transient OF could resolve with treatment and had a better outcome than persistent OF. Among the 13 deaths reported in our study, 46.2 % were in the early (<7 days) OF group compared to the late (>7 days) OF group (20%).

Organ Involvement. Pulmonary and renal failures were the most common organ involvements noted among our study group (32% each). This was followed by cardiovascular system (22%), coagulation system (8%) and central nervous system (6%) (Figure 5).

Figure 5. Organ failure by system.

Organ involvement and mortality. Fourteen (46.4%) patients had one or two OF and 17 (56.6%) had more than two OF (table 3). Comparison of the number of organ failures to mortality was statically significant (p<0.001) (Figure 6).

Figure 6. Outcome in patients with increasing organ involvement.

We found that there was an increase in incidence of mortality with an increase in the number of organs involved. There were no deaths in patients with up to two organ failures; it increased with increasing number of organs involved (Table 3).

Table 3. Organ failure and mortality.

The mortality rate was 70% (n=7) with three organ involvement, 80 % (n=4) with four and 100% with five OF.

Discussion

Severe acute pancreatitis is a systemic disease and characterized by acute onset and rapid progression, with a high incidence of complications and serious morbidity (20). An international multidisciplinary classification of acute pancreatitis severity is based on local and systemic determinants of severity. The local determinants relate to presence of pancreatic necrosis, and whether the necrosis is infected or sterile. The systemic determinants relate to whether there is organ failure or not, and if present, whether it is transient or persistent. The presence of both infected pancreatic necrosis and persistent organ failure has a greater impact on severity than either determinant alone. Based on these principles, the severity is classified as mild, moderate, severe or critical (21).The three most common systems involved are renal, lung, and cardiovascular system. Respiratory complications are frequent in acute pancreatitis and respiratory dysfunction is a major component of multiple organ dysfunction syndrome (22,23). In a population based study, 15.05% of patients with AP had a diagnosis of acute renal failure (24).

The present study showed that the difference in age was not significantly different between the groups. There are some studies which showed an association between advancing age as a predictor of organ failure and mortality. Wig et al. (6) studied 161 patients and concluded that age of the patients was a risk factor for multiple organ failure. Li et al. (25) studied 181 patients with SAP and found a correlation of age with OF (<.001). Frey et al²⁶ also showed that the number of complications was positively correlated with the age of patients. Older age and number of complications were strong predictors of organ failure among patients with SAP. Though we recorded a higher incidence of organ failures and mortality in a younger age group of 40-45, the difference was attributed to a small number of patients above 65 years in our study as compared to studies done in the western world.

The bedside index for severity in acute pancreatitis (BISAP) score represents a simple way to identify patients at risk for increased mortality and the development of intermediate markers of severity within 24 hours of presentation. In our series the BISAP score was significantly associated (p<.001) with organ failure as well as survival (p<.001). We found 9 out of 13 deaths in the >3 score group and four deaths at a BISAP score of 2 as compared to zero mortality in the BISAP score 1 and 0 group. Kim et al. (27) also compared BISAP, the serum procalcitonin (PCT), and other multifactorial scoring systems simultaneously, concluded that BISAP is more accurate for predicting the severity of acute pancreatitis than the serum procalcitonin, APACHE-II, Glasgow, and modified CT severity index (MCTSI) scores. Chen et al. (28) evaluated the accuracy of BISAP in predicting the severity and prognosis of acute pancreatitis (AP) in 497 Chinese patients. They conclude that BISAP score is valuable in predicting the severity of AP and prognoses of SAP in Chinese patients.

Contrast enhanced computed tomography (CECT) is considered the gold standard for the diagnosis of pancreatic necrosis and peripancreatic collections. CT assessment correlates with the clinical course of the disease and recognized variables of disease severity. We ordered CECT in all patients on the second or third day after admission rather than at the time of admission. Additional contrast-enhanced CT scans were ordered at intervals during the hospitalization to detect and monitor the course of intra-abdominal complications of acute pancreatitis, such as the development of organized necrosis, pseudocysts, and vascular complications including pseudoaneurysms. In our study CT severity index (CTSI) > 7 at admission did not correlate well with organ failure or mortality (p=NS), although the percentage of necrosis had significant correlation with organ failure. Our results are similar to many studies reported in the literature. Simchuk et al. (29) performed a study on 268 patients with acute pancreatitis. They concluded CTSI > 5 correlated significantly with death. Similar results were also obtained by Leung et al. (30) on 121 patients studied retrospectively, and they concluded that CTSI is superior to Ranson’s score and APACHE II score in predicting outcome in pancreatitis. However a few studies found no association between grade of necrosis and outcome of pancreatitis. Shinzeki et al. (31) did not find any correlation between necrosis evident on CECT at admission and outcome of SAP (p=0.061). Another study by Lankisch et al. (32) also did not find any correlation between necrosis and organ failure.

In our study pulmonary and renal were the most common organ failures observed (32% each). The total number of organ failures at admission was also significantly different in both groups (p=0.001), however none of the organ failures independently proved to be a significant predictor of mortality. MMOFS difference was found to be highly significantly correlated with organ failures and outcome. Among the 13 deaths reported in our study, 46.2 % were in the early (<7 days) OF group compared to the late (>7 days) OF group (20%). Our series also showed comparable results with other studies suggesting that early organ failure is the major predictor of poor outcome MMOFS difference was found to be highly significantly (ANOVA, p<0.001 each) correlated with organ failures and outcome. In our study no deaths occurred in the transient OF groups (early transient, late transient and transient deteriorating). We attributed this to the dynamics that transient OF could resolve with treatment and had a better outcome than persistent OF. Among the 13 deaths reported in our study, 46.2 % were in the early (<7 days) OF group compared to the late (>7 days) OF group (20%). Our series also showed comparable results with other studies suggesting that early organ failure is the major predictor of poor outcome (p=0.002) compared to late organ failure (p=0.400).

We found that early persistent OF had a 66.6% mortality as compared to persistent deteriorating organ failure which also had a very high mortality (72.2%). Very few studies have reported on the dynamics of OF with MMOFS. Johnson et al. (19) in a study of 290 patients with SAP had 116 patients with no OF and 147 patients with OF at the time of admission subdivided those with OF into those with persistent (OF lasting for>48 hours) and transient (OF lasting for<48hours) organ failure. Mortality was 36.3% in persistent and 5% in transient OF group. No patients without OF died.

On analysis of the 13 patients who expired, 4 patients died early (<7 days) and 9 deaths were late (>7 days). OF was the main cause of death in both groups, however all patients with sepsis died later. In the study of Yang et al. (33) the most important and common cause of death for patients with fulminant pancreatitis was multiple organ dysfunction syndrome, which usually was the consequence of systemic inflammation response syndrome in the early stage, and severe infection in the later stage, respectively.

Conclusions

Patients with persistent organ failure have a higher mortality. Early persisting and deteriorating organ failure had the worst outcome of among patients with acute pancreatitis. There was an increase in mortality with increasing number of organs involved. The extent of necrosis was directly related with the incidence of organ failure.

References

1. Bollen TL, Singh VK, Maurer R, Repas K, van Es HW, Banks PA, Mortele KJ. A comparative evaluation of radiologic and clinical scoring system in the early prediction of severity in acute pancreatitis. Am J Gastroenterol. 2012;107:612-9. [CrossRef] [PubMed]
2. Zyromski NJ. Necrotizing pancreatitis in 2010: an unifinished odyssey. Ann Surg. 2010; 251:794-5. [CrossRef] [PubMed]
3. Kylanpaa L, Rakonczay Z, O' Reilly DA. The clinical course of acute pancreatitis and the inflammatory mediators that drive it. Int J inflammation. 2012;2012:360685. [CrossRef] [PubMed]
4. Refinetti RA, Martinez R. Necrotising pancreatitis: update and when to operate. Clinical Medicine and Diagnostics. 2012;2:20-6.
5. Poves Prim I, Fabregat Pous J, García Borobia FJ, Jorba Martí R, Figueras Felip J, Jaurrieta Mas E. Early onset of organ failure is the best predictor of mortality in acute pancreatitis. Rev Esp Enform Dig (Madrid). 2004;96:705-13. [PubMed]
6. Wig JD, Bharathy KG, Kochhar R, Yadav TD, Kudari AK, Doley RP, Gupta V, Babu YR. Correlates of organ failure in severe acute pancreatitis. JOP. 2009;10:271-5. [PubMed]
7. Mole DJ, Olabi B, Robinson V, Garden OJ, Parks RW. Incidence of individual organ dysfunction in fatal acute pancreatitis: analysis of 1024 death records. HPB (Oxford). 2009;11:166-70. [CrossRef] [PubMed]
8. Halonen KI, Pettilä V, Leppäniemi AK, Kemppainen EA, Puolakkainen PA, Haapiainen RK. multiple organ dysfunction associated with severe acute pancreatitis. Crit Care Med. 2002;30:1274-9. [CrossRef] [PubMed]
9. Lytras D, Manes K, Triantopoulou C, Paraskeva C, Delis S, Avgerinos C, Dervenis C. Persistent early organ failure: defining the high-risk group of patients with severe acute pancreatitis? Pancreas. 2008;36:249-54. [CrossRef] [PubMed]
10. Vege SS, Gardner TB, Chari ST, Munukuti P, Pearson RK, Clain JE, Petersen BT, Baron TH, Farnell MB, Sarr MG. Low mortality and high morbidity in severe acute pancreatitis without organ failure: a case for revising the Atlanta classification to include moderately severe acute pancreatitis. Am J Gastroentrol. 2009;104:710-5. [CrossRef] [PubMed]
11. Frey CF, Zhou H, Harvey DJ, White RH. The incidence and case fatality rates of acute biliary, alcoholic and idiopathic pancreatitis in California 1994-2001. Pancreas. 2006;33:336-44. [CrossRef] [PubMed]
12. Buter A, Imrie CW, Carter CR, Evans S, McKay CJ. Dynamic nature of early organ dysfunction determines outcome in acute pancreatitis. Br J Surg. 2002;89:298-302. [CrossRef] [PubMed]
13. Mofidi R, Duff MD, Wigmore SJ, Madhavan KK, Garden OJ, Parks RW. Association between early systemic inflammatory response, severity of multiorgan dysfunction and death in acute pancreatitis. Br J Surg. 2006:93:738-44. [CrossRef] [PubMed]
14. Banks PA, Freeman ML. Practice guidelines in acute pancreatitis. Am J Gastroenterol. 2006;101:2379-2400. [CrossRef] [PubMed]
15. Pandol SJ, Saliya AK, Imrine CW, Banks PA. Acute pancreatitis :Bench to bed side : Gasroentrology. 2007;133:1127-51. [CrossRef] [PubMed]
16. de-Madaria E, Soler-Sala G, Lopez-Font I, Zapater P, Martínez J, Gómez-Escolar L, Sánchez-Fortún C, Sempere L, Pérez-López J, Lluís F, Pérez-Mateo M. Update of Atlanta classification of severity of acute pancreatitis: should a moderate category be included. Pancreatology. 2010;10: 613-9. [CrossRef] [PubMed]
17. Johnson CD, Abu-Hilal M. Persistent organ failure during the first week as a marker of fatal outcome in acute pancreatitis. Gut. 2004;53;1340-4. [CrossRef] [PubMed]
18. Mentula P, Kylanpaa ML, Kemppainen E. Early prediction of organ failure by combined markers in patients with acute pancreatitis. Br J Surg 2005; 92: 68-75. [CrossRef] [PubMed]
19. Garg PK, Madan K, Pande GK, Khanna S, Sathyanarayan G, Bohidar NP, Tandon RK. Association of extent and infection of pancreatic necrosis with organ failure and death in acute necrotizing pancreatitis. Clin Gastroentrol Hepatol. 2005;3:159-66. [CrossRef] [PubMed]
20. Pavlidis P, Crichton S, Lemmich Smith J, Morrison D, Atkinson S, Wyncoll D, Ostermann M. improved outcome of severe acute pancreatitis in the intensive care unit. Crit Care Res Pract. 2013;2013:897107. [CrossRef] [PubMed]
21. Dellinger EP, Forsmark CE, Layer P, et al. Determinant- based classification of acute pancreatitis severity: an international multidisciplinary consultation. Ann Surg. 2012;256:875-80. [CrossRef] [PubMed]
22. Chan YP, Ning JW, Je F. Establishment of critical period of severe acute pancreatitis associated lung injury. Hepatobiliary Pancreas Dis Int. 2009;8: 535-40. [PubMed]
23. Pastor CM, Matthy MA, Frossard JL. Pancreatitis associated acute lung injury – new insights. Chest. 2003;124: 2341-51. [CrossRef] [PubMed]
24. Lin HY, Lai JI, Lai YC, Lin PC, Chang SC, Tang GJ.Acute renal failure in severe pancreatitis: a population based study. Ups J Med Sci. 2011;116:155-9. [CrossRef] [PubMed]
25. Li XY, Wang XB, Liu XF, Li SG. Prevelence and risk factor of organ failure in patients with severe acute pancreatitis. World J Emerg Med. 2010;1(3):201-4. [PubMed]
26. Frey C, Zhou H, Harvey D. Co-morbidity is a strong predictor of early death andmulti-organ system failure among patients with acute pancreatitis. J Gastrointest Surg. 2007;11:733-42. [CrossRef] [PubMed]
27. Kim BG, Noh MH, Ryu CH, Nam HS, Woo SM, Ryu SH, Jang JS, Lee JH, Choi SR, Park BH. A comparison of the BISAP score and serum procalcitonin for predicting the severity of acute pancreatitis. Korean J Intern Med. 2013;28:322-9. [CrossRef] [PubMed]
28. Chen L, Lu G, Zhou Q, Zhan Q. Evaluation of the BISAP score in predicting severity and prognoses of acute pancreatitis in Chinese patients. Int Surg. 2013;98(1):6-12. [CrossRef] [PubMed]
29. Simchuk EJ, Traverso LW, Nukui Y, Kozarek RA. Computed tomography severity index is a predictor of outcomes for severe pancreatitis. Am J Surg. 2000;179:352-5. [CrossRef] [PubMed]
30. Leung TK, Lee CM, Lin SY, Chen HC, Wang HJ, Shen LK, Chen YY. Balthazar computed tomography severity index is superior to Ranson criteria and APACHE II scoring system in predicting acute pancreatitis outcome. World J Gastroenterol. 2005;11:6049-52. [PubMed]
31. Shinzeki M, Ueda T, Takeyama Y, Yasuda T, Matsumura N, Sawa H, Nakajima T, Matsumoto I, Fujita T, Ajiki T, Fujino Y, Kuroda Y. Predictors of early death in severe acute pancreatitis. J Gastroenterol. 2008;43:152-8. [CrossRef] [PubMed]
32. Lankisch PG, Pflichthofer D, Lehnick D. No strict correlation between necrosis and organ failyre in acute pancreatitis. Pancreas. 2000;20:319-22. [CrossRef] [PubMed]
33. Yang XN, Guo J, Lin ZQ, Huang L, Jin T, Wu W, Wen L, Zhang ZD, Xia Q, Hu WM. The study on causes of death in fulminant pancreatitis at early stage and late stage. Sichuan Da Xue Xue Bao Yi Xue Ban. 2011;42(5):686-90. [PubMed]

Reference as: Maheshwari N, Kumar A, Iqbal ZA, Mandal AK, Vyas A, Wig JD. Organ failure in acute pancreatitis and its impact on outcome in critical care. Southwest J Pulm Crit Care. 2015;10(5):253-64. doi: http://dx.doi.org/10.13175/swjpcc055-15 PDF