Sending Advisory Electronic Mail to Primary Care Staff, Addressing Low Metabolic Measures: Assessing the Health Outcomes for Patients Above Age 75
Phase: NA
Conditions: Weight Loss, Thinness, Malnutrition
Sponsor: Clalit Health Services

CLINICAL TRIAL PROTOCOL 
 METABOL_EMAIL 
 
ENGLISH VERSION 
 
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20/05/2015 
Electronic mail to clinic team, advising treatment 
modifications due to low metabolic indexes of 75+ years 
old patients: Effect on health measures. 
 
Principal Investigator: Nir Tsabar 
 
 
 
Clalit Health Services 
 
 
 
POB 131, Bat Hefer 4284200, Israel. 
 
 
 
 
 
 
 
Nir.Tsabar@Clalit.Org.IL 
 
Protocol Version #3 13/10/2015
 
Translated From Hebrew To English
 
 
 

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Investigators: 
Nir Tsabar,  MD, PhD; Yan Press, MD; Bracha Klein, MPA, RN; Yohana Rotman,  MPH, RN ;
Maya Vainshtein Tal, BSc, MHA ,RD; Yonatan Grossman, MD; Sofia Eilat, MD. 
Background: 
Technologic progress promotes large scale medical monitoring and intervention.  It enables 
increased attention to signs of nutritional deficiency. Notable examples include the monitoring of 
Body Mass Index (BMI), glycated hemoglobin (HbA1c%) and total serum cholesterol (herein 
referred to as cholesterol), which are integrated in the system of quality measures today (e.g. 
the Israeli National Program for Quality Indicators in Community, 2012). Monitoring and 
correcting caloric nutritional deficiencies are critical for optimal medical and nursing care, 
certainly at ages of 75 years and more (Hazzard's, 2009; Brunner & Suddarth's, 2013). 
 
Interactions between diseases, nutritional status and medical treatment are particularly 
complicated with advancing age. Old age often comes with multiple morbidity as well as 
increased vulnerability to medicine adverse effects. These may result in nutritional imbalance, 
which is detrimental to health and to daily living functions. Malnutrition is a major cause of 
vulnerability to stress (frailty). Intriguingly,  medicines that may harm the digestive system and 
nutritional status include those that are used frequently to improve 'quality indicators'. Examples 
include metformin, statins and many more. 
 
Hence, when monitoring health quality indicators, the authors believe it is important to 
search for optimal values - that include minimum levels (and not only maximum levels) for each 
physiologic indicator. In this trial, we will focus on BMI, HbA1c% and cholesterol. 
 
BMI is a widely used as an index of nutritional problems. In a meta-analysis which 
included 2.88 million people, the optimal BMI for overall survival was found to be 25-30 kg/m2 
(Flegal, 2013; Dixon, 2014 ; Pan, 2012). Rapid weight loss is a strong indicator for health and 
functional decline. Such a weight loss, especially with low BMI values, usually mandates 
investigation in order to eliminate the cause or  to limit its effect. A use of an automatic e-mail 
alert system was found to improve screening rate of malnourished patients in hospital 
(Giovannelli, 2014). 
 
Correlation between death and HbA1c% is U-shaped, with increased mortality under a 
6.5% level in patients taking two antidiabetic medicines (Currie, 2010). Among these patients, 
the optimal HbA1c% values of for survival were 7.5-8%. A substantial proportion of older adults 
with diabetes were found by Lipska et al. (2015) to be potentially overtreated.  
 
Correlation 
between 
death 
and 
cholesterol 
is 
also 
U-shaped 
(Iso, 
1989). 
Hypocholesterolemia is defined as cholesterol below 160 mg% and predicts increased mortality 
and morbidity (Hazzard's, p. 1341). We found no interventional study for this situation in the 
scientific literature. 
 
In our experience, Email alerts to doctors, regarding an over-tight control of specified 
diabetic patients by sulphonyl-urea medicines was followed by a reduction (from 3% to 1.8%) in 

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mortality comparing to a previous-year matched control group (unpublished observations). 
While this result was not statistically significant, it encouraged us to try this large scale 
interventional clinical trial. It also served us in assessing the size of the study population 
needed. 
Objectives: 
This study aims to determine whether reminding physicians and nurses about too low values of 
weight (as BMI), glucose (as HbA1c%) or cholesterol (as total serum cholesterol) improves 
survival and health of people older than 75 years. 
Study Hypotheses: 
A. Main Study Hypotheses: 
1. Compliance: Sending the intervention letter will cause a positive change in the 
relevant index (a rise in BMI, HbA1c%, or cholesterol, respectively). 
2. Health effect: Sending the intervention letter will cause a lower mortality rate in each 
study group compared to control groups, within a year. 
B. Other Study Hypotheses: compared to control groups, sending the intervention letter will 
cause improvement in morbidity and in secondary measures (see below).  
And in detail: 
Reducing  numbers of hospital stays and days of stays, numbers of physician and nurse visits 
with records of symptoms, number of dispensed medicines, medical expenses; in the HbA1c% 
intervention - reduced number of dispensed anti-diabetes medicines and reduced rate of 
measuring glucose levels of 70 mg/dL or less ; in the cholesterol intervention - reduced number 
of dispensed anti-cholesterol medicines. 
Increasing rate of weight measuring, BMI level, HbA1c% measuring, HbA1c% level, cholesterol 
measuring, cholesterol level; in the BMI intervention - percentage of patients evaluated by a 
nurse and counseled by a dietician. 
Methodology: 
Briefly: This prospective, randomized controlled trial, will compare BMI, HbA1c% and 
cholesterol values, rate of death, number of stays, days of stays, numbers of physician and 
nurse visits with records of symptoms such as "weakness", "dizziness" and total medical 
expenses of patients 75 years and older that their clinic staff will receive the email concerning 
them, versus patients that such email concerning them will not be sent. The trial has 3 different 
alerts: 1. An alert about a significant drop in BMI; 2. An alert about low HbA1c% level in patients 
treated with diabetes medicines; and 3. An alert about hypocholesterolemia in patients treated 
with cholesterol-lowering medicines. 

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Recruiting and inclusion criteria: 75 years and older at the beginning of the trial, clients of Clalit 
health Services at the North and the South districts, which will be identified thru the computer 
system (BO) as having one or more of the following: 
For alert no. 1: A drop in BMI of 2 Kg/m2 or more during the last two years and1 a BMI lower than 
23 Kg/m2 and1 no dietician counseling during the last year. 
For alert no. 2: HbA1c% of 6.5% or less and1 dispensing diabetes medicines during the last 2 
months. 
For alert no. 3: Total cholesterol less than 160 mg/dL and1 dispensing cholesterol-lowering 
medicines during the last 2 months. 
Exclusion criteria: 
General: 
Clients that an email address of their physician or nurse is not found. 
For alert no. 3: Clients with a diagnosis of myocardial infarction, ischemic heart disease, 
transient ischemic attack or cortical cerebrovascular accident. 
Study schedule: 
Sending the alerting mails will be done in one-session during 2015. 
Collecting the data will be done after one year. 
Publishing the main results will be done within another year at the latest (2017).  
Study procedures and methods: 
0. Preparations: 
A pre-trial email will be sent to the primary physicians on behalf of the district managements in 
order to inform about the trial. The text is provided in appendix No. 4. Feedback regarding 
receiving of the letter (full email boxes etc.) will be collected. Primary physicians would be able 
to choose to refrain from participating. 
1. Finding the participants: 
A. By using the Clalit health services computer system; an Excel table will be created; 
which will include these fields: Identity number; Name; Age; Sex; Clinic name; Primary 
physician name; Primary physician's email address; Nurse in charge at the clinic; email 
address of that nurse; Last BMI value of the last year; First BMI value of the previous 
year; Existence of a dietician visit on the last year; Last total cholesterol level; Whether 
dispensing of a cholesterol lowering medicine was recorded during the last 2 months; 
Name of (the last) cholesterol lowering medicine dispensed - with dose and quantity; 
Whether dispensing of a diabetes medicine was recorded during the last 2 months; 
                                                          
1 AND as intersection 

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Name of (the last) diabetes medicine dispensed - with dose and quantity; Whether 
diagnosed  with myocardial infarction; Whether diagnosed  with brain infarction; Whether 
diagnosed  with any cardiovascular disease. 
B. Participants for each separate inclusion group, according to the criteria above, will be 
identified. At this stage, a new list will be created, where the people who don't fit any of 
the criteria will be excluded. Some of the participants may fit more than one criterion. 
Hence 7 subgroups are created: 
  A) 
Patients that fit only alert 1. (Significant weight loss without dietitian assessment) 
  B)  
Patients that fit only alert 2. (A potentially too tight medicinal diabetes treatment) 
  C)  Patients that fit only alert 3. (A potentially too tight medicinal cholesterol lowering) 
  D) 
Patients that fit both alert 1. and alert 2. 
  E)  
Patients that fit both alert 1. and alert 3. 
  F)  
Patients that fit both alert 2. and alert 3. 
  G)  Patients that fit all alerts: 1. , 2. and 3. 
C. Population size: According to 11.2014 there are about 5000 patients that fit these 
criteria at North district and a similar number at the South district, totaling ~ 10,000. This 
provides a sufficient statistical power to examine the effect on the main hypotheses. (We 
used Pearson X2 test, assuming a 3% to 1.8% decline in death rates, as observed in a 
similar setting). 
2. Randomization: 
a. Randomization aims to get an unbiased and equal division into an intervention group 
and a control group. 
b. Randomization will be via computer: by adding a column in the excel table, with the 
function =ROUND(RAND(),0). This means 1:1 randomization to 0 (control) or 1 
(intervention letter). 
c. In order to avoid large differences in numbers of participants among the subgroups, 
the randomization procedure will repeat until the difference between control and 
intervention group is less than 2%. 
3. The alert emails will be prepared and sent via "Mail Merge" (of Microsoft Word) to the primary 
physicians and to the nurses in charge at the same clinics. The templates to those emails are 
shown in the appendix. Technical problems (e.g. full mailbox of recipients) will be recorded and 
managed promptly (- days, hopefully), by the principal investigator. 
4. Collecting outcome data: will be via the Clalit health services computer system. Data will 
include: 

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a. For all participants (control and trial groups), for the year that follows the intervention 
email: 
1. Mortality (of any cause) 
2. Days in stays (of any ward) 
3. Number of stays 
4. Number of physician visits 
5. Number of nurse visits with symptoms 
6. Number of all medicines dispensed 
7. Medical expenses  
8. Last BMI level and date 
9. Last cholesterol level and date 
10. Last HbA1c% level and date 
11. Glucose levels of 70 mg/dL or less, and dates 
b. For alert 1., for the year that follows the intervention email: 
1. Recording of functional estimation by a nurse 
2. Recording nutritional estimation by a nurse 
3. Recording dietitian consultation 
c. For alert 2. on the 3 months that follows the intervention email: 
1. Whether a diabetes medicine was dispensed, and if so:  
2. Name of diabetes medicine, dose and quantity 
d. For alert 3. on the 3 months that follows the intervention email: 
1. Whether a cholesterol lowering medicine was dispensed, and if so:  
2. Name of medicine, dose and quantity 
5. Data security: will be handled according to Clalit health systems procedures. The information 
will be saved in allowed secure servers only and will be available to authorized researchers only 
after their guidance and signing a non-disclosure commitment. When applicable, personal 
identification details will be omitted from the lists. 
6. Outcome analysis: 

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Categorical variables will be shown as frequencies and percentages. Continuous variables will 
be shown using standard distribution indices (e.g. average, standard deviation, median, etc.). 
Differences between the arms of the study will be examined using Chi-square test (or Fishers' 
exact test) for categorical variables and T-test (or Wilcoxon two sample test) for continuous 
variables. The statistical processing will be performed using Excel or SAS 9.2 software and will 
be statistically significant if P <.05 . 
7. Ethical issues of the trial: 
A waiver of the requirement to obtain a signed informed consent from participants is requested 
with the following reason: There is no contact with the patients, only unsheathing their existing 
data.   
A waiver of Form 11 is requested with the following reason: 
The family physician will be the one who would get the alert related to this trial in the first place.

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Appendixes: 
 
 

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Appendix 1. Template for email regarding a patient with BMI <23 Kg/m2 that lost ≥2 Kg/m2  
Email Subject:    
LIMIT-A trial alert: Signs of metabolic risk for your patient – Mrs./Mr. _________ , I.D. 
_________ 
Email Body: 
Dear Dr./Mrs./Mr. ________ , 
This letter is sent to you as a part of an authorized clinical trial. Details about the trial can be 
found in this link. Briefly: Patients aged 75+ with signs of a metabolic risk were found via 
computer. This detection included significant weight loss etc. After randomization to control 
and intervention groups, relevant alerts are sent for the intervention groups. In this trial we will 
check the effect of these alerts on treatment, health indexes and survival – a year from now. 
Mrs./Mr. _________ , I.D. _________, at ________ clinic of yours, was lately found to 
have low BMI level ( ____Kg/m2), with a significant drop from previous year level ( 
____Kg/m2). We ask you to consider re-estimation of his functional and medical 
condition and of his treatment, in order to prevent nutritional deterioration, and to 
consider referral to dietary counseling.  
We will be grateful for cooperation and will be happy to discuss any questions, 
 
Sincerely, 
 
 
Dr. Nir Tsabar (Principal investigator) 
 
 
4056670
-
052
 
 
 
fax 
8548890
-
04
 
 
 
Nir.Tsabar@Clalit.Org.IL 
 
Further background: 
In a meta-analysis which included 2.88 million people, the optimal BMI for overall survival was 
found to be 25-30 kg/m2 (Flegal, 2013; Dixon, 2014 ; Pan, 2012). Rapid weight loss is a strong 
indicator for ill health and functional decline. Such a weight loss, especially with low BMI values, 
usually mandates investigation in order to eliminate the cause or  to limit its effect. It might result 
from a combination of factors, some preventable, such as bacterial infection (in mouth, 
stomach etc.), smoking, alcohol use, feeding problem, constipation, medicines etc. 
Commonly used medicines that may cause malnutrition include metformin containing tablets, 
medicines that cause constipation and heartburn (e.g. calcium channel blockers, calcium 
tablets) and many more. 
 

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Appendix 2. Template for email regarding a patient with low HbA1c% that took an anti-
diabetic medicine. 
Email Subject:    
LIMIT-B trial alert: Low HbA1c% level of your patient – Mrs./Mr. _________ , I.D. 
_________ 
Email Body: 
Dear Dr./Mrs./Mr. ________ , 
This letter is sent to you as a part of an authorized clinical trial. Details about the trial can be 
found in this link. Briefly: Patients aged 75+ with signs of a metabolic risk were found via 
computer. This detection included low HbA1c% (<6.5%) with anti-diabetic medicines, etc. 
After randomization to control and intervention groups, relevant alerts are sent for the 
intervention groups. In this trial we will check the effect of these alerts on treatment, health 
indexes and survival – a year from now. 
Mrs./Mr. _________ , I.D. _________, at ________ clinic of yours, was lately found to 
take anti-diabetic medicine (_______) and the HbA1c% was ____ . This level is low for 
diabetic medical treatment. According to current guidelines and quality-indicators, a 
level somewhat below 8% is satisfying and safer.  
We ask you to consider re-estimation of his functional and medical condition and of his 
treatment, including lowering the dose of the anti-diabetic medicine.  
We will be grateful for cooperation and will be happy to discuss any questions, 
 
Sincerely, 
 
 
Dr. Nir Tsabar (Principal investigator) 
 
 
4056670
-
052
 
 
 
fax 
8548890
-
04
 
 
 
Nir.Tsabar@Clalit.Org.IL 
 
Further background: 
Correlation between death and HbA1c% is U-shaped, with increased mortality under the level of 
6.5% in patients taking two antidiabetic medicines (Currie, 2010). Among these patients, the 
optimal HbA1c% values of for survival were 7.5-8%. Updated quality-measures consider <8% is 
satisfying for diabetic patients of age 75+ years. 
 
 

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Appendix 3. Template for email regarding a patient with low cholesterol that took a 
cholesterol lowering medicine. 
Email Subject:    
LIMIT-C trial alert: Low cholesterol level of your patient – Mrs./Mr. _________ , I.D. 
_________ 
Email Body: 
Dear Dr./Mrs./Mr. ________ , 
This letter is sent to you as a part of an authorized clinical trial. Details about the trial can be 
found in this link. Briefly: Patients aged 75+ with signs of a metabolic risk were found via 
computer. This detection included low cholesterol (<160mg%) with cholesterol lowering 
medicines, etc. After randomization to control and intervention groups, relevant alerts are sent 
for the intervention groups. In this trial we will check the effect of these alerts on treatment, 
health indexes and survival – a year from now. 
Mrs./Mr. _________ , I.D. _________, at ________ clinic of yours, was lately found to 
take cholesterol lowering medicine (_______) and the total cholesterol was ____ . This 
level is low. (Hypocholesterolemia is defined when levels fall below 160mg%.)  
We ask you to consider re-estimation of his functional and medical condition and of 
his treatment, including lowering the dose of the cholesterol-lowering medicine.  
We will be grateful for cooperation and will be happy to discuss any questions, 
 
Sincerely, 
 
 
Dr. Nir Tsabar (Principal investigator) 
 
 
4056670
-
052
 
 
 
fax 
8548890
-
04
 
 
 
Nir.Tsabar@Clalit.Org.IL 
 
Further background: 
In old age, a total cholesterol level of less than 160mg% is related to a greater risk of 
functional decline and of death. Cholesterol level may be low due to a combination of factors, 
including reversible ones, such as bacterial infection, smoking, malnutrition, medicines and 
more. Cholesterol lowering medicines may cause nutritional insult and revising their dose and 
usage is warranted, especially with patients older than 75 years, and particularly if they suffer 
from possible adverse effects of these medicines.  Adverse effects include: musculo-skeletal - 
pain and gait problem, liver, digestive and respiratory tract symptoms and mental problems such 
as insomnia, depression etc. 
 
 

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Appendix 4. A preliminary email from the district medical management to primary 
physicians 
Subject: Research on "Effect of Electronic mail to clinic team, advising treatment modifications 
due to low metabolic indexes of 75+ years old patients, on health outcomes."   
Dear Doctor, 
malnutrition in the elderly is a significant cause of morbidity and mortality. 
In the next two months, email letters will be sent to you, as a part of a study that aims to find out 
whether alerting about low nutritional indexes (weight loss, low HbA1c% or low cholesterol) in 
elderly patients of yours. 
Details of the trial can be found in this link. 
We ask you to read carefully the emails you'll receive and to relate to the recommendations (of 
course, according to your professional judgment and to the details of each case). 
In case you are not willing to participate in this research - please notify the principal researcher 
Dr. Nir Tsabar,  as soon as possible. 
Thank you for your cooperation, 
 Sincerely, 
   Dr. ________, North/South district medical manager 
 
 

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Bibliography 
The National Program for Quality Indicators in Community Healthcare in Israel. 
http://www.health.gov.il/Publicationsfiles/qindicatorsReport2008-2010.pdf Accessed 29 Nov. 
2014. 
Glucomin patient leaflet. 
http://www.old.health.gov.il/units/pharmacy/Trufot/ShowAlon.asp?tmpPath=/units/pharmacy/truf
ot/alonim/Glucomin_pt_minor_changes_1402041326006.pdf Accessed 29 Nov. 2014. 
Cresor patient leaflet. 
http://www.old.health.gov.il/units/pharmacy/Trufot/ShowAlon.asp?tmpPath=/units/pharmacy/truf
ot/alonim/Crestor_5_10_20_40mg_Tablets_Pt_1405252220492.pdf Accessed 29 Nov. 2014. 
Afilalo J, Duque G, Steele R, et al. (2008). Statins for secondary prevention in elderly patients: a 
hierarchical bayesian meta-analysis. J Am Coll Cardiol. 51(1):37-45. 
Currie CJ, Peters JR, Tynan A, et al. (2010). Survival as a function of HbA(1c) in people with 
type 2 diabetes: a retrospective cohort study. Lancet. 375(9713):481-9.  
Dixon JB, Egger GJ, Finkelstein EA, Kral JG, Lambert GW. (2014). 'Obesity Paradox' 
misunderstands the biology of optimal weight throughout the life cycle. Baker International 
Journal of Obesity. Apr 15. 
Flegal KM, Kit BK, Orpana H, Graubard BI. (2013). Association of all-cause mortality with 
overweight and obesity using standard body mass index categories: a systematic review and 
meta-analysis. JAMA. 309(1):71-82. 
Giovannelli J, Coevoet V, Vasseur C, Gheysens A, Basse B, Houyengah F. (2014). How can 
screening for malnutrition among hospitalized patients be improved? An automatic e-mail alert 
system when admitting previously malnourished patients. Clin Nutr. Sep 18. 
Gutierrez J., Ramirez G., Rundek T., Sacco RL
( .
2012
 .)
Statin Therapy in the Prevention of 
Recurrent Cardiovascular Events A Sex-Based Meta-analysis .Arch Intern Med
 
 ,
172
 (
12
 ,)
-
909
919
.
 
Hinkle J.L., &. C. (2013). Brunner & Suddarth’s Textbook of Medical-Surgical Nursing (13 ed.). 
Philadelphia, Pa: Lippincott, Williams & Wilkins. 
Iribarren C., R. D. (1995). Low serum cholesterol and mortality. Which is the cause and which is 
the effect? Circulation , 92 (9), 2396-403. 
Iso H., J. D. (1989). Serum cholesterol levels and six-year mortality from stroke in 350,977 men 
screened for the multiple risk factor intervention trial. New England Journal of Medicine , 320 
(14), 904-10. 

Page
 
14
 
of
 
14  
METABOL_EMAIL  
  
 
Jeffrey Halter, J. O. (2009). Hazzard's Geriatric Medicine and Gerontology (6 ed.). New York: 
McGraw-Hill. 
Pan WH, Yeh WT, Chen HJ, Chuang SY, Chang HY, Chen L, Wahlqvist ML. (2012). The U-
shaped relationship between BMI and all-cause mortality contrasts with a progressive increase 
in medical expenditure: a prospective cohort study. Asia Pac J Clin Nutr. 21(4):577-87. 
Savarese G, Gotto AM Jr, Paolillo S, et al. (2013). Benefits of statins in elderly subjects without 
established cardiovascular disease: a meta-analysis. J Am Coll Cardiol. 62(22):2090-9.