Ilustrasi Kasus
Seorang laki-laki berumur 28 tahun, bekerja sebagai karyawan swasta dalam seminggu ini melakukan self quarantine dipenginapannya dikarenakan minggu lalu menghadiri pemakaman seseorang yang didiagnosa positif COVID-19.Setelah lima hari mengalami batuk kering, demam, pilek, dan sesak nafas,akhirnya inisiatif berobat ke Rumah Sakit. Dari hasil pemeriksan fisik:
- Sakit, cm; TD 110/80; RR 24x/menit, Temp; 39,9 Nadi 125x menit, saturasi O2 92% (oximetri portable)
- RBH (+) diparu kanan dan kiri,
- Hasil CXR terdapat infiltrate bilateral
- Serta ada riwayat fatty liver dari hasil USG Abdomen. Skor PORT 22%.
The Chest X Ray (CXR), when interpreted by a skilled physician like you, has a positive LR of 10 and negative LR of 0,5 for pneumonia ( ref.1) The patient’s CXR is positive
Reference :
- Chan SS, Mak PS, Shing KK, Chan PN,Ng WH, Rainer TH. Chest radiograph screening for severe acute respiratory syndrome in the ED. Am J Emerg Med 2005; 23(4):525-530.
- Louie L, Simor AE, Chong S, Detection of SARS CoV in Stool Specimens by Commercially Availeble Real Time RT-PCR Assays. Journal of Clinical Microbiology 2006; 44(11);4193-6
Dari kasus tersebut dapat dideskripsikan antara lain :
- Kemungkinan pertama peluang pneumonia jika CXR positif
The young men’s pretest probability of pneumonia is 22% (PORT Score).
His pretest odds are therefore p/(1-p) = 0,22/(1-0,22) = 0,28.
Bayes theorem tells us that pretest odds x LR = posttest odds
- Since his CXR is positive, we use the positive LR of 10, The posttest odds are given by : 0,28 x 10 = 2,8
- We can convert these odds back to a probability : Probability = Odds in favor/(odds in favor+odds against) = 2,8/3,8 = 0,74 = 74% (posttest probability)
- An X ray was warranted. The young man’s pretest probability is high such that even a positive CXR result would have change management. In other words, with a 74% probability of pneumonia
- Kemungkinan kedua peluang pneumonia jika CXR negative
The young men’s pretest probability of pneumonia is 22% (PORT Score).
His pretest odds are therefore p/(1-p) = 0,22/(1-0,22) = 0,28
Bayes theorem tells us that pretest odds x LR = posttest odds
- Since his CXR is negative, we use the negative LR of 0,5, The posttest odds are given by :2,8 x 0,5 = 1,4
- We can convert these odds back to a probability : Probability = Odds in favor/ (odds in favor+odds against) = 1,4/2,4 = 0,58= 58%(posttest probability)
- An X ray was warranted. The young man’s pretest probability is low such that even a negative CXR result would have no change management. In other words, with a 58% probability of have not pneumonia
- Kemungkinan ketiga peluang pneumonia jika CXR positif dan RT-PCR positif
The RT-PCR ,has a positive LR of 39 and negative LR of 0,62 for pneumonia (ref.2)
The patient’s RT-PCR is positive.
The young men’s pretest probability of pneumonia is 74% (CXR)
His pretest odds are therefore p/(1-p) = 0,74/(1-0,74) = 2,9.
Bayes theorem tells us that pretest odds x LR = posttest odds.
- Since his PCR is positive, we use the positive LR of 39, The posttest odds are given by :2,9 x 39 = 113
- We can convert these odds back to a probability :Probability = Odds in favor/ (odds in favor+odds against) = 113/114 = 0,99 = 99% An RT-PCR was warranted.
- The young man’s pretest probability is high such that even a positive RT-PCR result would have change management. In other words, with a 99% probability of C-19, the patient should be treated with an isolation ward.
- Kemungkinan keempat peluang pneumonia jika CXR positif dan RT-PCR negatif
The RT-PCR , has a positive LR of 39 and negative LR of 0,62 for pneumonia(ref.2)
The patient’s RT-PCR is negative
The young men’s pretest probability of pneumonia is 74% (CXR).
His pretest odds are therefore p/(1-p) = 0,74/(1-0,74) = 2,9.
Bayes theorem tells us that pretest odds x LR = posttest odds
- Since his PCR is negative, we use the negative LR of 0,62 The posttest odds are given by : 2,9 x 0,62 = 1,8
- We can convert these odds back to a probability :Probability = Odds in favor/ (odds in favor+odds against) = 1,8/2,8= 64%. In other words, with a 64% probability of do not have C-19
- Kemungkinan kelima peluang pneumonia jika CXR negatif dan RT-PCR positif
The RT-PCR ,has a positive LR of 39 and negative LR of 0,62 for pneumonia(ref.2)
The patient’s RT-PCR is positive The young men’s has probability of not pneumonia is 58% (CXR)
His pretest odds are therefore p/(1-p) = 0,58/(1-0,58) = 1,38.
Bayes theorem tells us that pretest odds x LR = posttest odds.
- Since his PCR is positive, we use the positive LR of 39 The posttest odds are given by : 1,38 x 39 = 54
- We can convert these odds back to a probability :Probability = Odds in favor/ (odds in favor+odds against) = 54/55= 98 An RT-PCR was warranted In other words, with a 98% probability of C-19
- Kemungkinan keeenam peluang pneumonia jika CXR negatif dan RT-PCR negatif
The RT-PCR ,has a positive LR of 39 and negative LR of 0,62 for pneumonia (ref.2)
The patient’s RT-PCR is negative
The young men’s has probability of not pneumonia is 58% (CXR).
His pretest odds are therefore p/(1-p) = 0,58/(1-0,58) = 1,38.
Bayes theorem tells us that pretest odds x LR = posttest odds.
- Since his PCR is negative, we use the negative LR of 0,62 The posttest odds are given by:1,38 x 0,62 = 0,86
- We can convert these odds back to a probability :Probability = Odds in favor/ (odds in favor+odds against) = 0,86/1,86=0,46 (46%) An RT-PCR was warranted. In other words, with a 46% probability of negative C-19
Kesimpulan
- Jika klinis mendukung tanda-tanda pneumonia, dan CXR positif, maka peluang kejadian COVID-19 (sebelum hasil RT-PCR keluar), adalah
- 99% peluang hasil PCR nya positif
- 64% peluang hasil PCR nya negatif
- Jika klinis mendukung tanda-tanda pneumonia, CXR negatif, maka peluang kejadian COVID-19 (sebelum RT-PCR keluar), adalah
- 98% peluang hasil PCR nya positif
- 46% peluang hasil PCR nya negatif
Saran:
Keputusan klinik, apakah pasien nya di rawat isolasi, bangsal biasa, atau karantina di rumah saja, ada pada dokter DPJP nya
