Eleventh issue
LMHI newsletter
10 June 2020
Testing the mini-repertory app
In the previous newsletter (tenth issue) we
presented a new mini-repertory for COVID-19 like illness, regarding the three
most prescribed homeopathic medicines: Arsenicum album (ars), Bryonia
(bry) and Gelsemium (gels). The repertorisation algorithm was based
on Bayes’ theorem and likelihood ratio (LR). The advantage of LR (= (prevalence
in medicine population) / (prevalence in remainder of the population) is that
it discriminates between medicines better than in our well-known repertories.
Some of the symptoms with their LRs are shown in Table 1.
The app supporting the repertorisation for
symptoms in the mini-repertory is available at the website address https://hpra.co.uk/.
Table 1: occurrence of four symptoms in the research population of 161 patients
and in populations responding well to Arsenicum album (ars), Bryonia (bry) and Gelsemium (gels), with the corresponding LRs.
Symptoms
|
Count
|
ars
|
LRars
|
bry
|
LRbry
|
gels
|
LRgels
|
n=
|
161
|
21
|
|
45
|
|
25
|
|
fatigue
|
87
|
11
|
0.96
|
20
|
0.77
|
20
|
1.62
|
dry cough
|
73
|
8
|
0.82
|
26
|
1.43
|
10
|
0.86
|
dyspnea
|
51
|
5
|
0.72
|
18
|
1.41
|
4
|
0.46
|
headache
|
48
|
6
|
0.95
|
17
|
1.41
|
9
|
1.26
|
An example demonstrates the difference
which LR makes: the symptom ‘fatigue’ was present in 87 out of all 161 cases,
and in 11 out of 21 ars cases, in 20 out of 45 bry cases and in
20 out of 25 gels cases. In the existing repertory this would have
resulted in bold entries for all three medicines under the rubric ‘Fatigue in
COVID-19’. In the Bayesian repertory we see LR= 1.62 for gels and
LR=0.77 for bry. This can be understood as a slight confirmation for gels
if the symptom is present and a slight contra-indication for bry. Such
differences can be demonstrated only by noting the same symptom in a
considerable number of cases who responded well to the same medicine.
The homeopathic principle that one symptom,
or a diagnosis, is insufficient to choose a homeopathic medicine accurately is
even more relevant for common symptoms. Using the existing repertory, the
combination of the symptoms ‘fatigue’ and ‘headache’ would not give us any
indication of the appropriate medicine. Only after finding a keynote symptom
for gels, like ‘Chill running up and down the back’, we would say: “Yes,
headache and fatigue confirm gels”. To be able to demonstrate that the
combination of fatigue and headache indicates, say, gels more than ars,
we would start by asking for symptoms that confirm gels, like ‘chill
running up and down the back’, or we might observe that the patient can hardly
keep his eyes open.
The Bayesian repertory enables better use
of common – and therefore relatively unimportant – symptoms. LR shows small but
clinically relevant differences between medicines which have the same typology
in the standard repertory, and a combination of such common symptoms can result
in larger differences between medicines. To give an example: the combination of
‘fatigue’ plus ‘headache’ results in a combined LR= 1.62 x 1.26 = 2.04 for gels
and LR= 0.96 x 0.95 = 0.91 for ars. The combined LR for gels now becomes
more than twice as large for gels than for ars.
With this procedure we can take a
combination of formerly quite useless symptoms and turn this into a meaningful
indication for specific medicines. This is especially useful if the patient has
no symptoms that clearly indicate a specific medicine. The downside of LR,
however, is that we have to make the necessary calculations. This is easily
performed in a computerised repertory, but where this is not available we can
use the app mentioned above.
The app contains the 20 most frequently
occurring symptoms from a collection of 161 COVID-19 cases. It calculates
combined LRs for specific medicines from a selection of these symptoms.
Currently, the app can differentiate between only three medicines: ars, bry
and gels. Other medicines and other symptoms can be added when enough
cases have been gathered. One symptom we would like to add is ‘loss of taste
and/or smell’, because this is a typical COVID-19 symptom, but the data we have
so far do not yet show a clear difference between medicines.
Testing the app with real cases
Recently, a collection of case studies
involving COVID-19 was published with 18 cases; these responded well to ars
(one case), bry (four cases), gels (12 cases) and Eupatorium
perfoliatum (eup-p) (one case).[1]
The cases were presented with clear descriptions of background, symptomatology
and outcome. This, as well as the fact that all cases except one responded to
the medicines present in the app, offered an opportunity to test the app with
the symptoms seen in these cases.
The advantage of frequently occurring
symptoms is that many patients have such symptoms. The disadvantage is that
precisely this fact also causes the medicines to be less well differentiated.
The clinical database we have so far contains 87 patients with ‘fatigue’ spread
over the 36 medicines prescribed in 161 cases. The symptom ‘fatigue, cannot
keep his eye open’, however, occurs in only 4 cases, all of which are gels.
For a first screening, e.g. with a standard
questionnaire, the symptom ‘fatigue’ would be more useful than ‘fatigue, cannot
keep his eyes open’ because the first occurs 22 times more frequently than the
latter. On the other hand, a first screening is useful only if it indicates
specific medicines that can be explored further.
However, it is vital that the first
screening does not lead us in the wrong direction. To check this we need
confirmed cases with an adequate number of common symptoms, combined with more
specific symptoms that give a clear picture of the effective medicine. The
common symptoms available in the app can be entered into this, and the output
of the app should not contradict the recommendation that results from a full
analysis of the case.
The
question was: does the use of only the most frequently occurring symptoms in an
initial screening give results that are consistent with the outcome of the
standard homeopathic method using all available symptoms, general and
particular?
How it works can be shown by case HK1.1.
The observed symptoms were:
1.
Slow onset and progression of
symptoms.
2.
Feeling irritable from the
cough; does not want to talk to anyone. Prefers to be alone.
3.
Obvious increase in thirst with
desire to drink warm water in large quantity.
4.
Generally ameliorated after
perspiration.
5.
Mainly dry cough, with very
occasional greenish sputum.
6.
Extremely bad pulsating temple
headache and middle chest pain aggravated from coughing.
7.
Cough aggravated by talking and
lying down, and after waking up in the morning.
8.
Cough associated with tickling
feeling in the throat, ameliorated by warm drinks.
Out of these 5 symptoms could be found in
the app:
1.
Thirst
2.
Dry cough
3.
Headache
4.
Chest pain < cough
5.
Cough < talking
The outcome of the app was “Strong
indication for Bryonia” and this was indeed the medicine that was prescribed.
An experienced homeopathic practitioner will recognise the medicine at first
sight because of symptoms like ‘irritable from cough’, ‘aversion to company’,
‘thirst for large quantities’ and ‘headache from cough’, which are not in the
app. However, with the combination of symptoms ‘thirst’, ‘dry cough’ and
‘headache’ the app would already have returned a “Moderate indication for
Bryonia”. This results from the following LRs for bry: LR=3.31 for
thirst; LR=1.43 for dry cough and LR=1.41 for headache. The combined LR for
these three symptoms is 3.31 x 1.43 x 1.41 = 6.67, high enough for a ‘moderate
indication’. ‘Chest pain < cough’ adds LR=3.61 and ‘Cough < talking’
LR=1.47, rendering a combined LR=35.4, representing a strong indication.
The outcome of all cases is shown in Table
2. If the combined LR of the selected symptoms was between 3 and 6, the
indication was ‘slight’; if the combined LR was between 6 and 10, the
indication was ‘moderate’ and if combined LR>10, the indication was
‘strong’.
Table 2: advice e of the app after entering the symptoms available in 18
cases. The ‘Nr case symptoms’ is the number of symptoms described for each
case. ‘Nr app symptoms’ is the number of these symptoms available in the app.
‘App advised medicine’ represents the advice of the app with intensity.
Case
|
prescribed medicine
|
Nr case symptoms
|
Nr app symptoms
|
App advised medicine
|
HK1.1
|
bry
|
8
|
5
|
bry (strong)
|
HK2.1
|
bry
|
12
|
5
|
bry (slight)
|
HK3.1
|
bry
|
18
|
6
|
bry (strong)
|
HK3.2
|
gels
|
14
|
5
|
gels (strong), bry (slight)
|
HK3.3
|
gels
|
11
|
4
|
gels (moderate)
|
HK3.4
|
gels
|
12
|
3
|
gels (strong)
|
HK3.5
|
gels
|
12
|
6
|
gels (strong), bry (slight)
|
HK4.1
|
gels
|
5
|
4
|
gels (strong)
|
HK4.2
|
gels
|
5
|
4
|
gels (slight), bry (slight)
|
HK4.3
|
ars
|
5
|
4
|
ars (slight), gels (slight)
|
HK4.4
|
gels
|
5
|
4
|
gels (moderate)
|
HK4.5
|
gels
|
7
|
4
|
gels (strong)
|
HK5.1
|
gels
|
8
|
4
|
gels (strong)
|
HK5.2
|
bry
|
9
|
6
|
bry (moderate), gels (moderate)
|
HK5.3
|
gels
|
6
|
3
|
bry (slight)
|
HK5.4
|
gels
|
6
|
3
|
bry (slight)
|
HK5.5
|
eup-p
|
4
|
4
|
bry (slight), gels (slight)
|
HK6.1
|
bry
|
21
|
10
|
bry (strong)
|
Discussion
This set of cases contained only one case
(HK5.5) that could not be handled by the app, because it contained no data for
that particular medicine (eup-p). In this case, the app gave only slight
indications, but did not contradict the choice based on a full homeopathic
evaluation by offering a moderate or strong indication for one of its own
medicines.
In the remaining 17 cases, for 11 (65%) the
recommendation of the app was entirely consistent with the full homeopathic
evaluation, giving a moderate or strong indication for the prescribed medicine.
In one case (HK5.2), a second medicine came up to the same degree; in this
case, a specific symptom, ‘coldness up and down the back’ (not available in the
app), clarified the choice of gels. In three cases (HK2.1, HK4.2, HK4.3)
the recommendation was consistent but with only a slight indication; it did
not, though, contradict the definitive choice. In these cases, the full
evaluation clarified the choice because specific symptoms indicated the correct
medicine. In two cases (HK5.3, HK5.4), the recommendation of the app slightly
contraindicated the correct choice which was made on the basis of specific
symptoms, such as ‘Heaviness of the eyelids’, which clearly indicated the
relevant medicine.
Our results confirm that the app can be
useful where there are no specific symptoms and that the most frequently
occurring symptoms can give useful indications for medicine selection in
COVID-19 cases. The app can be used as an addition to a full homeopathic
consultation and evaluation of symptoms.
There is another potential application for
this app. Suppose you encounter a new outbreak of COVID-19 disease and the
number of cases is more than you can handle. In this case, an inexperienced
practitioner (or homeopathic student) could carry out the first screening with
the app and an experienced practitioner could do any necessary additional
homeopathic consultation. This would save a lot of time.
As we collect more cases, the app can be
extended with more medicines and more symptoms. It will also be possible to
fine-tune the output of the app by changing cut-off values for specific
recommendations.
How to improve the app further?
We hope that our newsletters will encourage
you to use the app to see what value it has. Naturally, you will want to see
more medicines in the app, but this can only be achieved with your help, with
your cases.
Remember that we are not trying to prove
anything with this research, we want to improve our practice! In any
case you submit, you should be satisfied that one specific homeopathic medicine
was responsible for the improvement. Try to explain why this was the case.
We summarise below the minimum data necessary
for this project. We already had:
-
Severity of COVID-19
illness: Mild – Moderate – Severe –
critical
-
Is COVID-19 confirmed?
-
Medicine, with date of first
intake
-
Number of hours until onset of
improvement and/or until absence of fever after the start of the medicine
-
If possible at least 3-5
symptoms that were characteristic for the case
-
Pneumonia on X-ray or CAT
Also check:
-
Onset of complaints: how many
hours/days between first symptoms and the moment the disease aggravated
-
Fatigue/prostration/exhaustion;
where is the weakness located
-
Fear/anxiety
-
Restlessness
-
Fever, chill, or chill
alternating with fever
-
Thirst
-
Pain; where
-
Cough dry or moist
-
Dyspnea
-
Throat pain
-
Loss of taste and/or smell
- diarrhoea
The LMHI COVID-19 case collection team:
Lex Rutten, Bernardo Merizalde, Robbert van Haselen, Raj Kumar Manchanda,
Ashley Ross, Gustavo Cataldi, Altunay Agaoglu, Tiziana di Giampietro, Lefteris
Tapakis, Theodore Lilas, Peter Gold, Frederik Schroyens, José Eizayaga
References
[1] To, Ka Lun Aaron; Fok YYY. Homeopathic Clinical Features of 18
Patients in COVID-19 Outbreaks in Hong Kong. Homeopathy. 2020;109.
Source: https://www.lmhi.org/Documents/News/Collecting%20clinical%20case%20newsletter%20nr_11.docx
To access the mini-repertorisation app: https://hpra.co.uk/