The following illustration is from our case based blended learning ecosystem, the online component of which happens in social media groups such as whatsapp.
Here is a previous working prototype linked from our book chapter:
http://medicinedepartment.
The section below is a discussion in the group by one of the students with roll number 35 among the 150 in their batch online dashboard linked here :
[7/29, 2:32 PM] Rishitha Kims 2019:
_Comparison of enoxaparin with placebo for the prevention of venous thromboembolism in acutely ill medical patients._
*Patient* - 1102 hospitalized patients older than 40 years. The primary outcome could be assessed in 866 patients.
Given 40 mg of enoxaparin, 20 mg of enoxaparin, or placebo subcutaneously once daily for 6 to 14 days.
*Intervention* -
~40 mg of enoxaparin : 291 / 866
~20 mg of enoxaparin : 287 / 866
*Comparator* -
~placebo : 288 / 866
*Results* -
1. The incidence of venous thromboembolism was significantly lower in the group that received _40 mg_ of _enoxaparin_ [16 of 291 patients] than in the group that received _placebo_ [43 of 288 patients]
2. There was no significant difference in the incidence of venous thromboembolism between the group that received _20 mg_ of _enoxaparin_ (43 of 287 patients] and the placebo group
3. The incidence of adverse effects did not differ significantly between the placebo group and either enoxaparin group.
4. By day 110,
-50 patients had died in the placebo group (13.9 percent)
-51 had died in the 20-mg group (14.7 percent)
-and 41 had died in the 40-mg group (11.4 percent);
the differences were not significant.
[7/29, 3:27 PM] Rishitha Kims 2019: PICO-
"Patient, Intervention, Comparator, Outcomes and :
P stands for the number of patients in the human experiment that participated
I stands for number of people in the intervention group.
C stands for number of people in the "comparator" group ideally those who received placebo
O stands for outcome in each group of people.
[7/29, 4:21 PM] Rakesh Biswas: Thanks Rishitha.
Very well done 👏👏
So what are your learnings from this data?
Although this is not in patients of DVT but still it was a very useful study because critically ill patients (especially as the recent covid patients that we saw) may develop thrombus formation in small vessels as a part of the septic inflammatory response.
Thanks for sharing this study in such a nice manner. Very useful. Do let me know your take home message and I shall let you know mine
[7/29, 4:48 PM] Rishitha Kims 2019: Thank you sir.
So,
We can hereby conclude that prophylactic treatment with 40 mg of enoxaparin subcutaneously per day safely, effectively reduces the risk of venous thromboembolism in patients with acute medical illness.
Since the case study does not involve the condition of DVT, the subject needs to be studied further.
Hence, the following link would provide the relatable content.
[7/29, 4:59 PM] Rakesh Biswas: Alright now look at point 4
What happened after 110 days inspite of treatment or no treatment (placebo)?
Everyone of these groups an equal number of people died? So did the treatment really matter?
[7/29, 5:30 PM] Rishitha Kims 2019: Sir, when the test statistic is not big enough to reject the hypothesis of no treatment effect, investigators often report no statistically significant difference.
The ability to detect a treatment effect with a given level of confidence depends on the size of the treatment effect, the variability within the population, and the *size of the samples* used in the study. Just as bigger samples make it more likely that you will be able to detect an effect, smaller sample sizes make it harder.
The distinction between positively demonstrating that a treatment had no effect and failing to demonstrate that it did have an effect is subtle but very important, especially in the light of the small numbers of subjects included in most clinical studies.
I have compiled this information from :
A good maximum sample size is usually around 10% of the population, as long as this does not exceed 1000.
So in our research, the sample size is around 30% which can yield appropriate results and marginally equal in all the treatments.
[7/29, 5:39 PM] Rakesh Biswas: Yes so assuming that the study you shared around anticoagulants in critical illness, had the right sample size, do you feel that it will be useful to anticoagulate all patients of critical illness in our ICUs as an equal number died in all groups regardless of their anticoagulation?
[7/29, 6:58 PM] Rishitha Kims 2019: No sir. Many factors are taken into consideration to assess whether the treatment is efficient or not.
Sample size gets to be of major importance in clinical study.
[7/29, 7:01 PM] Rakesh Biswas: Try to talk about the critical illness anticoagulant study that you shared.
What factors do you think suggests that anticoagulation could be useful in all critical illness patients ?
[7/29, 7:09 PM] Rakesh Biswas: Is there something wrong with the sample size of the study you shared in PICO format?
[7/29, 7:09 PM] Rishitha Kims 2019: No sir. The sample size is appropriate.
[7/29, 7:12 PM] Rishitha Kims 2019: Because of the high risk of thrombotic complications (TCs) during SARS-CoV-2 infection, several scientific societies have proposed to increase the dose of preventive anticoagulation.
But,
I think we cant only rely on sample size to assess whether the treatment would be effective without having appropriate information about relationship between the dose of anticoagulant therapy and the incidence of thrombotic complications.
[7/29, 7:16 PM] Rakesh Biswas: So is this conclusion for another issue?
That of anticoagulants in critically ill Covid patients? Would you like to share any covid study with adequate and appropriate sample size where they have managed to scientifically prove that anticoagulation is effective in critically ill Covid patients? If you are not able to find one such well done study then can we conclude that scientific societies may not always base their guidleines on science but based on other factors such as market forces or convenience?
[7/29, 7:20 PM] Rishitha Kims 2019: Yes sir. I have taken criticall ill COVID patients into consideration.
I could find a case study which says High-dose prophylactic anticoagulation is associated with a reduction in thrombotic complications in critically ill patients with COVID-19 without an increased risk of hemorrhage.
This information has been taken from:
538 patients included,
104 patients experienced a total of 122 thrombotic complications. Pulmonary embolism accounted for 52% of the recorded TCs.
[7/29, 7:20 PM] Rakesh Biswas: In response to your last paragraph here, the study that you shared in PICO format above did have appropriate information that you yourself pasted about the dose of anticoagulants and incidence of thrombotic complications?
[7/29, 7:24 PM] Rishitha Kims 2019: Yes sir, the information i displayed about the dose and incidence is appropriate, as stated in the study.
[7/29, 7:44 PM] Rakesh Biswas: In that case if the study was appropriate what would be your take home message from the study?
Should we anticoagulate all critically ill patients as it significantly reduces the incidence of pulmonary embolism over placebo or should we not anticoagulate because there is no significant difference in the mortality?
[7/29, 7:45 PM] Rakesh Biswas: Please share it in the PICO format that you shared for the non covid study
[7/29, 8:04 PM] Rishitha Kims 2019: Since the risk of thrombotic complications is high in critically ill patients, having Pulmonary embolism to contribute highest, we shall anticoagulate all critically ill patients over placebo irrespective of insignificant difference in the mortality in the above discussed case.
But it has been that the dose of anticoagulant to be increased progressively based on thrombotic risk factors that include obesity, high oxygen demand, need for mechanical ventilation, and biomarkers of major inflammation or hypercoagulability, despite the lack of evidence supporting this strategy.
Because, despite the use of regular prophylactic anticoagulation, the proportion of hospitalized patients experiencing thrombotic complications ranges from 18% to 37%, say some studies as mentioned in this:
[7/29, 8:05 PM] Rishitha Kims 2019: Sorry sir, but i am unable to compile data to assess this case study in the PICO format.
[7/29, 8:18 PM] Rakesh Biswas: So then is the study worth it?
[7/29, 8:21 PM] Rakesh Biswas: Why do you think those critically ill people are having pulmonary embolism and dying eventually after 110 days regardless of placebo or anticoagulant used?
Is it possible that it's not the coagulopathy which is killing them but the sepsis cascade which is also producing coagulopathy as a side effect?
[7/29, 9:59 PM] Rishitha Kims 2019: Severe corona virus maintains common features to sepsis.
Most critically ill patients admitted to ICU showed a dysregulated host response characterized by hyperinflammation, alterations in the coagulation, and dysregulation in the immune response that further contribute to MODS (Multi organ dysfunction failure), like occurs in sepsis.
Due to virus infection and to MODS in some cases, many patient have meet the Third International Consensus Definitions for Sepsis.
Some common characteristics with sepsis of respiratory origin, such as dense mucus secretions in airways, diffuse alveolar damage, increased pulmonary inflammation, and high levels of systemic proinflammatory cytokines and microthrombosis, probably as consequence of the increase in angiotensin II and angiotensin-converting enzyme 2 interaction and high levels of interleukin (IL)–6 and other proinflammatory cytokines contributing to *COAGULOPATHY.*
So, thereby we can say that coagulopathy is a side effect for the sepsis cascade because of the viral infection.
It is clear that hyperinflammation and coagulopathy contribute to disease severity and death in these patients.
[7/29, 10:02 PM] Rakesh Biswas: Good read.
But the side effect of coagulopathy may not be responsible for the deaths, which are more likely due to the sepsis multi organ failure
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