Orris
attachment
attachment

Interpret

Reading File
Finding Sources
Searching PubMed

"non-alcoholic fatty liver disease" AND "grade" AND "ultrasound"

Reading File
Finding Sources
Finding Sources
Searching the Web

Grade II fatty liver ultrasound meaning management NAFLD

Here is a detailed interpretation of this ultrasound report for Mr. Prateek Mahajan, 37-year-old male:

Ultrasound Abdomen & Pelvis — Report Interpretation

Organ-by-Organ Findings

OrganFindingSignificance
Liver15.2 cm, increased echogenicity, no focal lesions; portal vein (PV) & common bile duct (CBD) normal; no biliary dilatationAbnormal — Grade II fatty infiltration
GallbladderDistended, no stones/wall thickening/pericholecystic fluidNormal
PancreasHead & body normal; tail obscured by bowel gasPartially visualized — no concerning finding seen
Spleen9.1 cm, normal echo, no focal lesionsNormal
Right Kidney10.1 cm, normal, no calculi/hydronephrosisNormal
Left Kidney9.8 cm, normal, no calculi/hydronephrosisNormal
Urinary BladderDistended, no wall thickening, no calculiNormal
ProstateNormal size and echotextureNormal
Free fluidNoneNormal

Key Finding: Grade II Fatty Liver (Moderate Hepatic Steatosis)

This is the only abnormal finding. Fatty liver is graded on ultrasound based on echogenicity (brightness of liver compared to kidney):
GradeSonographic FeaturesFat Content (approx.)
Grade I (Mild)Slightly increased liver echogenicity; diaphragm and vessels still visible~5–33%
Grade II (Moderate)Moderately increased echogenicity; slightly obscured portal vein walls and diaphragm~33–66%
Grade III (Severe)Markedly increased echogenicity; diaphragm and posterior liver poorly visualized>66%
At Grade II, fat makes up roughly one-third to two-thirds of the liver. The hallmark on ultrasound is diffuse increased echogenicity (the liver appears brighter than the kidney), with beginning obscuration of vessel margins. This is consistent with what the radiologist describes as "normal in size and increased echo-texture."
The liver measures 15.2 cm — this is within or at the upper limit of normal (normal sagittal span ~14–17 cm at the midclavicular line), so hepatomegaly is not present here (Yamada's Textbook of Gastroenterology).

What Causes Grade II Fatty Liver?

The most common causes in a 37-year-old male, in order of likelihood:
  1. Non-alcoholic fatty liver disease (NAFLD) — strongly associated with insulin resistance, obesity, metabolic syndrome, dyslipidemia, and type 2 diabetes
  2. Alcoholic fatty liver disease — requires clinical history
  3. Other: medications (steroids, amiodarone), hypothyroidism, hepatitis C

Clinical Implications

  • Grade II is a warning stage — actionable but reversible with lifestyle changes
  • The absence of focal lesions, normal portal vein, and no biliary dilatation is reassuring — no signs of cirrhosis or portal hypertension at this time
  • Recommended next steps:
    • Blood tests: Liver function tests (ALT, AST, GGT, ALP, bilirubin), fasting lipid profile, fasting glucose/HbA1c, TSH, Hepatitis B & C serology
    • Fibroscan (transient elastography) or FIB-4 score to assess for underlying fibrosis/inflammation (NASH) — ultrasound alone cannot differentiate simple steatosis from steatohepatitis
    • BMI and waist circumference assessment
    • Lifestyle modification: Weight loss of 5–10% body weight significantly reduces hepatic fat; Mediterranean diet, alcohol avoidance, aerobic exercise (150–300 min/week)
    • Follow-up ultrasound in 6–12 months to assess response
    • No specific drug treatment is indicated at this stage unless metabolic co-morbidities (diabetes, dyslipidemia) are present

Summary

The scan is otherwise entirely normal. The only concern is moderate (Grade II) fatty liver, which is common in adults his age and is typically driven by metabolic factors. It is not an emergency, but warrants a metabolic workup and dedicated lifestyle intervention to prevent progression to non-alcoholic steatohepatitis (NASH), fibrosis, or cirrhosis.

How to stop metformin in a 60 year old male taking it from previous 5 years now presents with normal hba1c and fasting blood sugar on 90

Finding Sources
Searching PubMed

"metformin" AND "discontinuation" AND "type 2 diabetes" AND "glycemic control"

Searching the Web

when to stop metformin type 2 diabetes normal HbA1c guidelines deprescribing

Finding Sources
Reading Web Page

https://diabetesonthenet.com/wp-content/uploads/3.-Ali_Depre…

Here is a structured clinical approach to stopping metformin in this patient:

Deprescribing Metformin in a 60-Year-Old with Normalised Glycaemia

First: Confirm the Context

Before stopping, establish why his sugars are normal:
QuestionRelevance
Has he lost significant weight / changed diet?T2DM remission is achievable with weight loss ≥10–15%
Is his HbA1c truly in the normal range (≤5.6% / ≤38 mmol/mol) or just "well-controlled"?Different implications
Fasting glucose 90 mg/dL (5 mmol/L) — normal and not over-treatedReassuring
Any renal impairment (eGFR)?May be an independent reason to stop
Is metformin the only drug, or are there others?Deprescribing order matters
Any hypoglycaemic episodes?Unlikely with metformin alone (low hypo risk) but relevant if on combination
A fasting glucose of 90 mg/dL and normal HbA1c on metformin monotherapy is a valid indication for deprescribing, especially if driven by lifestyle improvement.

The Deprescribing Protocol

Metformin has a low hypoglycaemia risk, so the approach is a gradual stepwise dose reduction rather than abrupt cessation.
Typical metformin dose progression to stop:
StepActionDuration
Step 1Reduce by 500–1000 mg (e.g., from 1000 mg BD → 500 mg BD or 1000 mg OD)3 months
Step 2Check HbA1c. If still at target → reduce further (e.g., 500 mg OD)3 months
Step 3Check HbA1c. If still normal → stop completely—
Follow-upRecheck HbA1c 3 months after stoppingOngoing
Each dose reduction step should be separated by ~3 months to allow HbA1c to reflect the change (HbA1c lags by 8–12 weeks).

Monitoring After Stopping

  • Symptoms of hyperglycaemia: excessive thirst, polyuria, fatigue — counsel patient to watch for these
  • HbA1c at 3 months post-cessation — this is the key safety check
  • Fasting glucose periodically if patient has a glucometer
  • If HbA1c rises above target after stopping → restart at lowest effective dose or consider lifestyle intensification

Key Considerations at Age 60

FactorImplication
Age 60 — not yet "elderly frail"Standard deprescribing approach appropriate
5 years on metforminHis beta-cell reserve may be partially preserved if lifestyle is good
Metformin's pleiotropic benefitsIt has cardiovascular-neutral to mildly beneficial effects; also reduces risk of some cancers (colon, breast) — worth discussing
Renal function (eGFR)Must be checked; if eGFR <30 ml/min, stopping is mandatory anyway
B12 levelsLong-term metformin (>3–5 years) causes B12 malabsorption in ~30% — check B12 and supplement if low

When NOT to Stop Yet

  • HbA1c is merely "well-controlled" (e.g., 6.5–7%) on metformin — the drug is doing its job
  • No evidence of sustained lifestyle change that would maintain euglycaemia off the drug
  • Patient on combination therapy — sulphonylureas and insulin should be deprescribed before metformin, as they carry higher hypo risk

Bottom Line

In this 60-year-old with a normal HbA1c and fasting glucose of 90 mg/dL on metformin monotherapy, deprescribing is appropriate. The recommended approach is:
  1. Reduce dose by 500–1000 mg every 3 months
  2. Check HbA1c at each step
  3. Stop completely once the lowest dose still keeps levels normal, then recheck at 3 months
  4. Check B12 — after 5 years on metformin this is due regardless
  5. Counsel on lifestyle maintenance — this is what will sustain remission after stopping

55year old female presents with leg swelling which resolves on taking rest and has no pain

Reading File
Finding Sources
Finding Sources
Searching the Web

55 year old female bilateral leg swelling painless resolves with rest differential diagnosis causes

Finding Sources
Finding Sources
Reading File
Reading File
This is a clinically rich presentation. Here is a systematic interpretation:

55-Year-Old Female — Painless Leg Swelling Relieved by Rest

Characterising the Oedema — What the History Already Tells Us

FeatureClinical Significance
PainlessRules out DVT, cellulitis, acute arterial ischaemia, compartment syndrome
Resolves with rest / limb elevationStrongly suggests venous or systemic (hydrostatic) oedema — NOT lymphoedema (lymphoedema does NOT respond to elevation)
55-year-old femalePost-menopausal age → venous insufficiency, cardiac, thyroid, or hormonal causes more common
Bilateral assumedPoints to systemic causes; unilateral would push toward local venous/lymphatic pathology

Differential Diagnosis (Most to Least Likely)

🔴 Top Priority — Must Rule Out First

1. Chronic Venous Insufficiency (CVI) Most likely diagnosis in a 55-year-old female. Caused by incompetent venous valves → ambulatory venous hypertension → oedema. Key features:
  • Worse after prolonged standing/sitting, improves with leg elevation
  • Pitting oedema, typically at ankles, extending upward
  • May have varicose veins, skin hyperpigmentation, eczema around medial malleolus
  • No pain unless complicated by ulcers or superficial thrombophlebitis
2. Orthostatic / Dependent Oedema Fluid accumulation due to gravity from prolonged standing or sitting (common in perimenopausal women). Fully resolves with rest. No underlying pathology.

🟡 Systemic Causes — Screen For These

CauseClue
Heart failure (right-sided or biventricular)Bilateral pitting oedema, JVP raised, breathlessness, orthopnoea
HypothyroidismNon-pitting/myxoedema, fatigue, cold intolerance, dry skin, constipation — common in women this age
Hypoalbuminaemia (nephrotic syndrome, cirrhosis, malnutrition)Bilateral soft pitting oedema, frothy urine (nephrotic), jaundice (liver)
Renal failure / CKDPeriorbital oedema too, hypertension
AnaemiaHigh-output state can cause oedema

🟢 Other Causes to Consider

CauseNotes
MedicationsAmlodipine (CCBs), NSAIDs, steroids, hormones (HRT/OCPs), gabapentin, pioglitazone — ask full drug history
Lymphoedema (early/Stage I)Soft pitting at this stage, may mimic venous oedema; but typically does NOT resolve with overnight elevation
LipedemaBilateral symmetric fatty enlargement of legs, spares feet; NOT pitting, NOT relieved by elevation; common in women; often misdiagnosed as oedema
Pelvic mass / compressionBilateral venous outflow obstruction — gynaecological cause important at this age
Pregnancy-related / post-menopausal hormonalLess relevant unless on HRT

Approach — History and Examination

Key questions to ask:
  • Bilateral or unilateral? Which part of leg?
  • Time of day — worse in evening? (venous) or constant? (lymphatic/systemic)
  • Any breathlessness, orthopnoea, PND? (cardiac)
  • Weight gain? Fatigue? Cold intolerance? (hypothyroid)
  • Frothy urine? (nephrotic)
  • Any medications, especially CCBs or HRT?
  • History of varicose veins, DVT, pelvic surgery?
  • BMI — obesity strongly associated with CVI and dependent oedema
Examination:
  • Pitting vs non-pitting (press for 5 seconds over medial malleolus)
  • Stemmer sign (inability to pinch skin at base of 2nd toe — positive in lymphoedema)
  • JVP, S3 gallop, crackles (cardiac)
  • Skin: hyperpigmentation, lipodermatosclerosis, varicosities (CVI)
  • Abdomen: ascites, hepatomegaly (liver disease)
  • Thyroid examination

Investigations

TestPurpose
Serum albumin, LFTsLiver disease, hypoalbuminaemia
Urine dipstick + spot urine protein:creatinineNephrotic syndrome
TSHHypothyroidism — high yield in 55F
BNP / NT-proBNPHeart failure screen
FBCAnaemia
Renal function (creatinine, eGFR)CKD
Venous duplex ultrasound (lower limbs)Gold standard for CVI; rules out DVT
ECG + EchoIf cardiac cause suspected
Pelvic ultrasoundIf pelvic mass suspected

Most Likely Diagnosis in This Case

Given:
  • Painless oedema
  • Relieves completely with rest (elevation-responsive)
  • 55-year-old female (post-menopausal)
  • No mention of breathlessness, jaundice, or systemic symptoms
→ Chronic Venous Insufficiency or simple dependent/orthostatic oedema is the most probable cause, with hypothyroidism and calcium channel blocker use being important and easily treatable differentials to exclude first with basic bloods.
— Harrison's Principles of Internal Medicine 22E; Sabiston Textbook of Surgery, 11th Ed

Create a quick reference guide for oedema workup

Asking for Preferences
Reading File
Finding Sources
Finding Sources
Running Command

mkdir -p /home/daytona/workspace/oedema-guide && ls /home/daytona/workspace/

Writing File

~/oedema-guide/build_oedema_guide.py


from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm, mm
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, KeepTogether
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
from reportlab.platypus import PageBreak

OUTPUT = "/home/daytona/workspace/oedema-guide/Oedema_Workup_Quick_Reference.pdf"

# ── Colours ──────────────────────────────────────────────────────────────────
NAVY      = colors.HexColor("#1a3a5c")
TEAL      = colors.HexColor("#1a7d8e")
LIGHT_TEAL= colors.HexColor("#d4eef2")
AMBER     = colors.HexColor("#e07b1a")
AMBER_LT  = colors.HexColor("#fef3e2")
RED       = colors.HexColor("#c0392b")
RED_LT    = colors.HexColor("#fdecea")
GREEN     = colors.HexColor("#1e7d4b")
GREEN_LT  = colors.HexColor("#e6f4ed")
GREY_LT   = colors.HexColor("#f5f5f5")
GREY_MID  = colors.HexColor("#d0d0d0")
WHITE     = colors.white
BLACK     = colors.black

W, H = A4

doc = SimpleDocTemplate(
    OUTPUT,
    pagesize=A4,
    leftMargin=1.5*cm, rightMargin=1.5*cm,
    topMargin=2.0*cm, bottomMargin=1.8*cm,
    title="Oedema Workup Quick Reference",
    author="Orris Medical"
)

styles = getSampleStyleSheet()

# Custom paragraph styles
def S(name, parent='Normal', **kw):
    return ParagraphStyle(name, parent=styles[parent], **kw)

title_style   = S("Title2", fontSize=20, textColor=WHITE, alignment=TA_CENTER,
                   fontName="Helvetica-Bold", spaceAfter=2)
subtitle_style= S("Sub",   fontSize=9,  textColor=colors.HexColor("#b0d4db"),
                   alignment=TA_CENTER, fontName="Helvetica")
h1_style      = S("H1",    fontSize=11, textColor=WHITE, fontName="Helvetica-Bold",
                   spaceAfter=1, spaceBefore=1)
h2_style      = S("H2",    fontSize=9,  textColor=NAVY,  fontName="Helvetica-Bold",
                   spaceAfter=2, spaceBefore=4)
body_style    = S("Body",  fontSize=8,  textColor=BLACK, fontName="Helvetica",
                   spaceAfter=2, leading=11)
small_style   = S("Small", fontSize=7.5,textColor=colors.HexColor("#444444"),
                   fontName="Helvetica", leading=10)
bold_style    = S("Bold",  fontSize=8,  textColor=BLACK, fontName="Helvetica-Bold",
                   spaceAfter=2)
red_bold      = S("RedBold",fontSize=8, textColor=RED,   fontName="Helvetica-Bold")
green_bold    = S("GreenBold",fontSize=8,textColor=GREEN,fontName="Helvetica-Bold")
amber_bold    = S("AmberBold",fontSize=8,textColor=AMBER,fontName="Helvetica-Bold")
footer_style  = S("Footer", fontSize=7, textColor=colors.grey,
                   alignment=TA_CENTER)

USABLE_W = W - 3*cm   # left+right margins

# ── Helper: section header band ──────────────────────────────────────────────
def section_header(text, bg=NAVY):
    data = [[Paragraph(text, h1_style)]]
    t = Table(data, colWidths=[USABLE_W])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), bg),
        ("TOPPADDING",   (0,0), (-1,-1), 5),
        ("BOTTOMPADDING",(0,0), (-1,-1), 5),
        ("LEFTPADDING",  (0,0), (-1,-1), 8),
    ]))
    return t

def teal_header(text):
    return section_header(text, bg=TEAL)

def hr():
    return HRFlowable(width="100%", thickness=0.5, color=GREY_MID, spaceAfter=3)

# ── Title block ───────────────────────────────────────────────────────────────
def build_title():
    data = [[
        Paragraph("OEDEMA WORKUP", title_style),
        Paragraph("Quick Reference Guide for General Practitioners", subtitle_style)
    ]]
    t = Table([[
        Paragraph("OEDEMA WORKUP", title_style)
    ]], colWidths=[USABLE_W])
    t.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,-1), NAVY),
        ("TOPPADDING",(0,0),(-1,-1), 14),
        ("BOTTOMPADDING",(0,0),(-1,-1), 6),
        ("LEFTPADDING",(0,0),(-1,-1), 10),
    ]))
    sub = Table([[Paragraph("Quick Reference Guide  •  General Practitioners", subtitle_style)]],
                colWidths=[USABLE_W])
    sub.setStyle(TableStyle([
        ("BACKGROUND",(0,0),(-1,-1), TEAL),
        ("TOPPADDING",(0,0),(-1,-1), 5),
        ("BOTTOMPADDING",(0,0),(-1,-1), 5),
    ]))
    return [t, sub]

# ── STEP 1: Classify ──────────────────────────────────────────────────────────
def build_step1():
    elems = []
    elems.append(Spacer(1, 6))
    elems.append(section_header("STEP 1 — CLASSIFY THE OEDEMA"))
    elems.append(Spacer(1, 4))

    data = [
        [Paragraph("<b>Feature</b>", bold_style),
         Paragraph("<b>Pitting</b>", bold_style),
         Paragraph("<b>Non-Pitting</b>", bold_style)],
        [Paragraph("Press test (5s)", small_style),
         Paragraph("Pit remains >10s", small_style),
         Paragraph("Pit disappears rapidly", small_style)],
        [Paragraph("Texture", small_style),
         Paragraph("Soft, fluctuant", small_style),
         Paragraph("Firm, rubbery / doughy", small_style)],
        [Paragraph("Causes", small_style),
         Paragraph("Cardiac, renal, hepatic,\nvenous, drugs, low albumin", small_style),
         Paragraph("Lymphoedema, myxoedema,\nlipedema", small_style)],
        [Paragraph("Elevation response", small_style),
         Paragraph("✓ Usually resolves", small_style),
         Paragraph("✗ Minimal or absent", small_style)],
    ]
    col_w = [USABLE_W*0.28, USABLE_W*0.36, USABLE_W*0.36]
    t = Table(data, colWidths=col_w)
    t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (-1,0), LIGHT_TEAL),
        ("FONTNAME",     (0,0), (-1,0), "Helvetica-Bold"),
        ("ROWBACKGROUNDS",(0,1),(-1,-1), [WHITE, GREY_LT]),
        ("GRID",         (0,0), (-1,-1), 0.4, GREY_MID),
        ("TOPPADDING",   (0,0), (-1,-1), 4),
        ("BOTTOMPADDING",(0,0), (-1,-1), 4),
        ("LEFTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ]))
    elems.append(t)
    elems.append(Spacer(1, 3))

    # Unilateral vs bilateral banner
    side_data = [
        [Paragraph("<b>UNILATERAL SWELLING</b>", bold_style),
         Paragraph("<b>BILATERAL SWELLING</b>", bold_style)],
        [Paragraph("• DVT / thrombophlebitis\n• Cellulitis\n• Baker's cyst rupture\n"
                   "• Lymphoedema (primary/secondary)\n• Popliteal artery aneurysm\n"
                   "• Tumour / lymph node compression", small_style),
         Paragraph("• Heart failure (most common)\n• Chronic venous insufficiency\n"
                   "• Hypothyroidism / myxoedema\n• Hypoalbuminaemia (nephrotic, cirrhosis)\n"
                   "• Medications\n• Dependent / orthostatic\n• Lipedema", small_style)],
    ]
    side_t = Table(side_data, colWidths=[USABLE_W/2, USABLE_W/2])
    side_t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (0,0), RED_LT),
        ("BACKGROUND",   (1,0), (1,0), LIGHT_TEAL),
        ("FONTNAME",     (0,0), (-1,0), "Helvetica-Bold"),
        ("GRID",         (0,0), (-1,-1), 0.4, GREY_MID),
        ("TOPPADDING",   (0,0), (-1,-1), 5),
        ("BOTTOMPADDING",(0,0), (-1,-1), 5),
        ("LEFTPADDING",  (0,0), (-1,-1), 7),
        ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ]))
    elems.append(side_t)
    return elems

# ── STEP 2: History red flags ─────────────────────────────────────────────────
def build_step2():
    elems = []
    elems.append(Spacer(1, 6))
    elems.append(section_header("STEP 2 — HISTORY: KEY QUESTIONS & RED FLAGS"))
    elems.append(Spacer(1, 4))

    hx_data = [
        [Paragraph("<b>Question</b>", bold_style),
         Paragraph("<b>Positive finding → Think</b>", bold_style)],
        [Paragraph("Onset & duration?", small_style),
         Paragraph("Acute (<72h) → DVT, cellulitis, heart failure decompensation", small_style)],
        [Paragraph("Worse with standing/sitting?", small_style),
         Paragraph("Yes → Chronic venous insufficiency or dependent oedema", small_style)],
        [Paragraph("Relieves with elevation/rest?", small_style),
         Paragraph("Yes → Venous/systemic. No → Lymphoedema", small_style)],
        [Paragraph("Painful?", small_style),
         Paragraph("Yes → DVT, cellulitis, gout, lipodermatosclerosis", small_style)],
        [Paragraph("Breathlessness / orthopnoea / PND?", small_style),
         Paragraph("→ Heart failure (biventricular or right-sided)", small_style)],
        [Paragraph("Frothy urine / periorbital puffiness?", small_style),
         Paragraph("→ Nephrotic syndrome", small_style)],
        [Paragraph("Fatigue, weight gain, cold intolerance?", small_style),
         Paragraph("→ Hypothyroidism", small_style)],
        [Paragraph("Jaundice, alcohol, known liver disease?", small_style),
         Paragraph("→ Cirrhosis / hepatic hypoalbuminaemia", small_style)],
        [Paragraph("Recent immobility / travel / surgery?", small_style),
         Paragraph("→ DVT", small_style)],
        [Paragraph("Medications?", small_style),
         Paragraph("CCBs (amlodipine), NSAIDs, steroids, HRT,\ngabapentin, pioglitazone, minoxidil", small_style)],
        [Paragraph("Pelvic symptoms / history of cancer?", small_style),
         Paragraph("→ Pelvic mass obstructing venous/lymphatic return", small_style)],
    ]
    t = Table(hx_data, colWidths=[USABLE_W*0.38, USABLE_W*0.62])
    t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (-1,0), LIGHT_TEAL),
        ("FONTNAME",     (0,0), (-1,0), "Helvetica-Bold"),
        ("ROWBACKGROUNDS",(0,1),(-1,-1), [WHITE, GREY_LT]),
        ("GRID",         (0,0), (-1,-1), 0.4, GREY_MID),
        ("TOPPADDING",   (0,0), (-1,-1), 3),
        ("BOTTOMPADDING",(0,0), (-1,-1), 3),
        ("LEFTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ]))
    elems.append(t)

    # Red flags box
    elems.append(Spacer(1, 5))
    rf_data = [[
        Paragraph("🚨  RED FLAGS — Urgent referral / same-day assessment", ParagraphStyle(
            "rf_hdr", fontSize=8.5, textColor=WHITE, fontName="Helvetica-Bold")),
    ],[
        Paragraph(
            "• Acute unilateral painful swollen leg → rule out DVT (Wells score + D-dimer / USS)\n"
            "• Breathlessness + bilateral oedema → acute heart failure\n"
            "• Fever + warm erythematous leg → cellulitis / necrotising fasciitis\n"
            "• Rapid onset + rash + bronchospasm → anaphylaxis\n"
            "• Leg oedema + haemoptysis → PE with right heart strain",
            small_style)
    ]]
    rf_t = Table(rf_data, colWidths=[USABLE_W])
    rf_t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (-1,0), RED),
        ("BACKGROUND",   (0,1), (-1,-1), RED_LT),
        ("TOPPADDING",   (0,0), (-1,-1), 5),
        ("BOTTOMPADDING",(0,0), (-1,-1), 5),
        ("LEFTPADDING",  (0,0), (-1,-1), 8),
        ("BOX",          (0,0), (-1,-1), 0.8, RED),
    ]))
    elems.append(rf_t)
    return elems

# ── STEP 3: Examination ───────────────────────────────────────────────────────
def build_step3():
    elems = []
    elems.append(Spacer(1, 6))
    elems.append(section_header("STEP 3 — FOCUSED EXAMINATION"))
    elems.append(Spacer(1, 4))

    ex_data = [
        [Paragraph("<b>Finding</b>", bold_style),
         Paragraph("<b>Diagnosis to consider</b>", bold_style)],
        [Paragraph("Pitting oedema — ankles/legs", small_style),
         Paragraph("Cardiac, renal, hepatic, venous, drugs", small_style)],
        [Paragraph("Non-pitting, rubbery oedema", small_style),
         Paragraph("Lymphoedema (Stage II+), myxoedema", small_style)],
        [Paragraph("Stemmer sign +ve", small_style),
         Paragraph("Lymphoedema (unable to pinch skin at base of 2nd toe)", small_style)],
        [Paragraph("Raised JVP + S3 + crackles", small_style),
         Paragraph("Heart failure", small_style)],
        [Paragraph("Ascites + spider naevi + jaundice", small_style),
         Paragraph("Cirrhosis", small_style)],
        [Paragraph("Periorbital puffiness", small_style),
         Paragraph("Nephrotic syndrome, hypothyroidism", small_style)],
        [Paragraph("Thyroid enlargement + bradycardia + dry skin", small_style),
         Paragraph("Hypothyroidism", small_style)],
        [Paragraph("Varicose veins + skin hyperpigmentation + lipodermatosclerosis", small_style),
         Paragraph("Chronic venous insufficiency", small_style)],
        [Paragraph("Warm, tender, erythema, tracking", small_style),
         Paragraph("Cellulitis / lymphangitis", small_style)],
        [Paragraph("Positive Homan's sign (low sensitivity)", small_style),
         Paragraph("Consider DVT (use Wells score — do not rely on sign alone)", small_style)],
        [Paragraph("Symmetric bilateral leg fat (spares feet)", small_style),
         Paragraph("Lipedema — NOT true oedema", small_style)],
    ]
    t = Table(ex_data, colWidths=[USABLE_W*0.44, USABLE_W*0.56])
    t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (-1,0), LIGHT_TEAL),
        ("FONTNAME",     (0,0), (-1,0), "Helvetica-Bold"),
        ("ROWBACKGROUNDS",(0,1),(-1,-1), [WHITE, GREY_LT]),
        ("GRID",         (0,0), (-1,-1), 0.4, GREY_MID),
        ("TOPPADDING",   (0,0), (-1,-1), 3),
        ("BOTTOMPADDING",(0,0), (-1,-1), 3),
        ("LEFTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ]))
    elems.append(t)
    return elems

# ── STEP 4: Investigations ────────────────────────────────────────────────────
def build_step4():
    elems = []
    elems.append(Spacer(1, 6))
    elems.append(section_header("STEP 4 — INVESTIGATIONS (TIERED APPROACH)"))
    elems.append(Spacer(1, 4))

    # Tier 1
    elems.append(Paragraph("TIER 1 — First-Line (All patients with unexplained oedema)", h2_style))
    t1_data = [
        [Paragraph("<b>Test</b>", bold_style), Paragraph("<b>Detects</b>", bold_style)],
        [Paragraph("FBC", small_style), Paragraph("Anaemia, infection", small_style)],
        [Paragraph("Serum albumin + LFTs", small_style),
         Paragraph("Hypoalbuminaemia, hepatic disease", small_style)],
        [Paragraph("Renal profile (creatinine, urea, eGFR, electrolytes)", small_style),
         Paragraph("CKD, nephrotic syndrome", small_style)],
        [Paragraph("Urine dipstick + spot urine PCR", small_style),
         Paragraph("Proteinuria → nephrotic (PCR >300 mg/mmol)", small_style)],
        [Paragraph("TSH", small_style),
         Paragraph("Hypothyroidism — very high yield in women >50 y", small_style)],
        [Paragraph("BNP / NT-proBNP", small_style),
         Paragraph("Heart failure (NT-proBNP >125 pg/mL is abnormal)", small_style)],
        [Paragraph("Fasting glucose / HbA1c", small_style),
         Paragraph("Diabetic nephropathy context", small_style)],
    ]
    t1 = Table(t1_data, colWidths=[USABLE_W*0.47, USABLE_W*0.53])
    t1.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (-1,0), GREEN_LT),
        ("FONTNAME",     (0,0), (-1,0), "Helvetica-Bold"),
        ("ROWBACKGROUNDS",(0,1),(-1,-1), [WHITE, GREY_LT]),
        ("GRID",         (0,0), (-1,-1), 0.4, GREY_MID),
        ("TOPPADDING",   (0,0), (-1,-1), 3),
        ("BOTTOMPADDING",(0,0), (-1,-1), 3),
        ("LEFTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ]))
    elems.append(t1)

    elems.append(Spacer(1, 5))
    elems.append(Paragraph("TIER 2 — Directed by Clinical Suspicion", h2_style))
    t2_data = [
        [Paragraph("<b>Test</b>", bold_style), Paragraph("<b>When to order</b>", bold_style)],
        [Paragraph("Venous duplex USS (lower limbs)", small_style),
         Paragraph("Suspected DVT, chronic venous insufficiency", small_style)],
        [Paragraph("ECG + Echocardiogram", small_style),
         Paragraph("Raised BNP, clinical heart failure", small_style)],
        [Paragraph("Chest X-ray", small_style),
         Paragraph("Suspected cardiac failure, pleural effusion", small_style)],
        [Paragraph("D-dimer", small_style),
         Paragraph("Low–moderate Wells score for DVT (do NOT test if high pre-test probability)", small_style)],
        [Paragraph("Pelvic / abdominal USS", small_style),
         Paragraph("Suspected pelvic mass, liver / renal pathology", small_style)],
        [Paragraph("24-hr urine protein / renal biopsy", small_style),
         Paragraph("Confirmed nephrotic range proteinuria", small_style)],
        [Paragraph("Lymphoscintigraphy", small_style),
         Paragraph("Suspected lymphoedema — specialist referral", small_style)],
        [Paragraph("Lipid profile", small_style),
         Paragraph("Nephrotic syndrome (associated hyperlipidaemia)", small_style)],
    ]
    t2 = Table(t2_data, colWidths=[USABLE_W*0.47, USABLE_W*0.53])
    t2.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (-1,0), AMBER_LT),
        ("FONTNAME",     (0,0), (-1,0), "Helvetica-Bold"),
        ("ROWBACKGROUNDS",(0,1),(-1,-1), [WHITE, GREY_LT]),
        ("GRID",         (0,0), (-1,-1), 0.4, GREY_MID),
        ("TOPPADDING",   (0,0), (-1,-1), 3),
        ("BOTTOMPADDING",(0,0), (-1,-1), 3),
        ("LEFTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ]))
    elems.append(t2)
    return elems

# ── STEP 5: Diagnosis-specific management ────────────────────────────────────
def build_step5():
    elems = []
    elems.append(Spacer(1, 6))
    elems.append(section_header("STEP 5 — DIAGNOSIS-SPECIFIC MANAGEMENT SUMMARY"))
    elems.append(Spacer(1, 4))

    mgmt = [
        [Paragraph("<b>Diagnosis</b>", bold_style),
         Paragraph("<b>First-line management</b>", bold_style),
         Paragraph("<b>Refer if…</b>", bold_style)],
        [Paragraph("Heart failure", small_style),
         Paragraph("Loop diuretic (furosemide), ACEI/ARB,\nbeta-blocker, salt restriction", small_style),
         Paragraph("EF <40%, refractory, new diagnosis", small_style)],
        [Paragraph("Chronic venous insufficiency", small_style),
         Paragraph("Compression stockings (class II, 20–30 mmHg),\nelevation, skin care", small_style),
         Paragraph("Ulceration, failed conservative Rx", small_style)],
        [Paragraph("DVT", small_style),
         Paragraph("DOAC (rivaroxaban / apixaban) — start same day;\nconsider LMWH bridge in cancer", small_style),
         Paragraph("Proximal DVT, PE signs, cancer, pregnancy", small_style)],
        [Paragraph("Hypothyroidism", small_style),
         Paragraph("Levothyroxine; recheck TSH in 6–8 weeks", small_style),
         Paragraph("Cardiac comorbidity, elderly, resistant", small_style)],
        [Paragraph("Nephrotic syndrome", small_style),
         Paragraph("Low-salt diet, loop diuretic, ACEi (antiproteinuric)\nstatin, anticoagulation if albumin <20 g/L", small_style),
         Paragraph("All new cases → nephrology", small_style)],
        [Paragraph("Cirrhosis / hepatic oedema", small_style),
         Paragraph("Salt restriction, spironolactone ± furosemide,\navoid NSAIDs, treat underlying cause", small_style),
         Paragraph("Refractory ascites, hepatorenal syndrome", small_style)],
        [Paragraph("Drug-induced", small_style),
         Paragraph("Stop or switch offending drug (e.g. switch\namlodipine to another antihypertensive)", small_style),
         Paragraph("Persists after drug stopped >4 weeks", small_style)],
        [Paragraph("Lymphoedema", small_style),
         Paragraph("Complex decongestive therapy (CDT),\nmulti-layer compression bandaging", small_style),
         Paragraph("All cases → lymphoedema specialist", small_style)],
        [Paragraph("Dependent oedema / CVI", small_style),
         Paragraph("Leg elevation, compression, ambulation,\nweight loss, review medications", small_style),
         Paragraph("No improvement after 3 months", small_style)],
    ]
    t = Table(mgmt, colWidths=[USABLE_W*0.24, USABLE_W*0.44, USABLE_W*0.32])
    t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (-1,0), LIGHT_TEAL),
        ("FONTNAME",     (0,0), (-1,0), "Helvetica-Bold"),
        ("ROWBACKGROUNDS",(0,1),(-1,-1), [WHITE, GREY_LT]),
        ("GRID",         (0,0), (-1,-1), 0.4, GREY_MID),
        ("TOPPADDING",   (0,0), (-1,-1), 3),
        ("BOTTOMPADDING",(0,0), (-1,-1), 3),
        ("LEFTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ]))
    elems.append(t)
    return elems

# ── Wells DVT Score ───────────────────────────────────────────────────────────
def build_wells():
    elems = []
    elems.append(Spacer(1, 6))
    elems.append(teal_header("WELLS SCORE FOR DVT (Quick Reference)"))
    elems.append(Spacer(1, 4))

    w_data = [
        [Paragraph("<b>Clinical feature</b>", bold_style), Paragraph("<b>Score</b>", bold_style)],
        [Paragraph("Active cancer (treatment within 6 months or palliative)", small_style), Paragraph("+1", small_style)],
        [Paragraph("Paralysis, paresis or recent plaster immobilisation of lower extremity", small_style), Paragraph("+1", small_style)],
        [Paragraph("Bedridden ≥3 days or major surgery within 4 weeks", small_style), Paragraph("+1", small_style)],
        [Paragraph("Localised tenderness along deep venous system", small_style), Paragraph("+1", small_style)],
        [Paragraph("Entire leg swollen", small_style), Paragraph("+1", small_style)],
        [Paragraph("Calf swelling >3 cm vs asymptomatic side (measured 10 cm below tibial tuberosity)", small_style), Paragraph("+1", small_style)],
        [Paragraph("Pitting oedema confined to symptomatic leg", small_style), Paragraph("+1", small_style)],
        [Paragraph("Collateral superficial veins (non-varicose)", small_style), Paragraph("+1", small_style)],
        [Paragraph("Alternative diagnosis at least as likely as DVT", small_style), Paragraph("<b>−2</b>", bold_style)],
    ]
    wt = Table(w_data, colWidths=[USABLE_W*0.84, USABLE_W*0.16])
    wt.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (-1,0), LIGHT_TEAL),
        ("FONTNAME",     (0,0), (-1,0), "Helvetica-Bold"),
        ("ROWBACKGROUNDS",(0,1),(-1,-1), [WHITE, GREY_LT]),
        ("GRID",         (0,0), (-1,-1), 0.4, GREY_MID),
        ("TOPPADDING",   (0,0), (-1,-1), 3),
        ("BOTTOMPADDING",(0,0), (-1,-1), 3),
        ("LEFTPADDING",  (0,0), (-1,-1), 6),
        ("ALIGN",        (1,0), (1,-1), "CENTER"),
        ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ]))
    elems.append(wt)

    score_data = [[
        Paragraph("<b>Score ≤0:</b> Low probability → D-dimer; if negative, DVT excluded", small_style),
        Paragraph("<b>Score 1–2:</b> Moderate → D-dimer; if positive, do USS", small_style),
        Paragraph("<b>Score ≥3:</b> High probability → USS directly (skip D-dimer)", small_style),
    ]]
    st = Table(score_data, colWidths=[USABLE_W/3]*3)
    st.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (0,0), GREEN_LT),
        ("BACKGROUND",   (1,0), (1,0), AMBER_LT),
        ("BACKGROUND",   (2,0), (2,0), RED_LT),
        ("GRID",         (0,0), (-1,-1), 0.4, GREY_MID),
        ("TOPPADDING",   (0,0), (-1,-1), 5),
        ("BOTTOMPADDING",(0,0), (-1,-1), 5),
        ("LEFTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",       (0,0), (-1,-1), "MIDDLE"),
    ]))
    elems.append(Spacer(1, 3))
    elems.append(st)
    return elems

# ── Special populations ───────────────────────────────────────────────────────
def build_special():
    elems = []
    elems.append(Spacer(1, 6))
    elems.append(teal_header("SPECIAL POPULATIONS"))
    elems.append(Spacer(1, 4))

    sp_data = [
        [Paragraph("<b>Population</b>", bold_style),
         Paragraph("<b>Key considerations</b>", bold_style)],
        [Paragraph("Women >50 y (peri/post-menopausal)", small_style),
         Paragraph("Hypothyroidism high yield (check TSH). HRT-related. CVI common. Lipedema often misdiagnosed.", small_style)],
        [Paragraph("Elderly / frail", small_style),
         Paragraph("Polypharmacy (CCBs, steroids). Cardiac failure. Hypoalbuminaemia from malnutrition. Low diuretic threshold to cause AKI.", small_style)],
        [Paragraph("Pregnancy", small_style),
         Paragraph("Physiological oedema common in 3rd trimester. DVT risk 5× increased. Pre-eclampsia if hypertension + proteinuria.", small_style)],
        [Paragraph("Cancer patients", small_style),
         Paragraph("DVT / PE risk (Trousseau syndrome). Lymphoedema post-surgery/radiation. Pelvic mass obstruction. Hypoalbuminaemia.", small_style)],
        [Paragraph("Patients with CKD", small_style),
         Paragraph("Avoid NSAIDs. Furosemide doses may need to be higher. Refer nephrology if rapid progression.", small_style)],
    ]
    sp_t = Table(sp_data, colWidths=[USABLE_W*0.28, USABLE_W*0.72])
    sp_t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (-1,0), LIGHT_TEAL),
        ("FONTNAME",     (0,0), (-1,0), "Helvetica-Bold"),
        ("ROWBACKGROUNDS",(0,1),(-1,-1), [WHITE, GREY_LT]),
        ("GRID",         (0,0), (-1,-1), 0.4, GREY_MID),
        ("TOPPADDING",   (0,0), (-1,-1), 3),
        ("BOTTOMPADDING",(0,0), (-1,-1), 3),
        ("LEFTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ]))
    elems.append(sp_t)
    return elems

# ── Footer note ───────────────────────────────────────────────────────────────
def build_footer():
    elems = []
    elems.append(Spacer(1, 8))
    elems.append(HRFlowable(width="100%", thickness=0.5, color=GREY_MID))
    elems.append(Spacer(1, 3))
    elems.append(Paragraph(
        "Sources: Harrison's Principles of Internal Medicine 22E (2025) · Sabiston Textbook of Surgery 11E · "
        "Goldman-Cecil Medicine · NICE Guidelines CG144 (Heart Failure) · ESC Guidelines 2023 (Heart Failure, PE)  |  "
        "For clinical use by qualified practitioners only. Always apply clinical judgement.",
        footer_style))
    return elems

# ── Assemble document ─────────────────────────────────────────────────────────
story = []
story += build_title()
story += build_step1()
story += build_step2()
story += build_step3()
story += build_step4()
story += build_step5()
story += build_wells()
story += build_special()
story += build_footer()

doc.build(story)
print(f"PDF generated: {OUTPUT}")
Running Command

cd /home/daytona/workspace/oedema-guide && python build_oedema_guide.py

Running Command

ls -lh /home/daytona/workspace/oedema-guide/

Your quick reference guide is ready:

Oedema Workup Quick Reference

PDF Document · PDF

What's included in the guide:

SectionContent
Step 1 — ClassifyPitting vs non-pitting table; Unilateral vs bilateral differential diagnosis
Step 2 — History12-question key history table with diagnostic pointers; Red flags box (DVT, acute HF, cellulitis, PE)
Step 3 — Examination12-sign examination table with diagnoses (Stemmer sign, JVP, S3, varicosities, etc.)
Step 4 — InvestigationsTiered approach: Tier 1 (all patients) + Tier 2 (directed by suspicion)
Step 5 — Management9 diagnoses with first-line treatment and referral criteria
Wells DVT ScoreFull scoring table + colour-coded interpretation (Low/Moderate/High)
Special PopulationsWomen >50y, elderly, pregnancy, cancer, CKD
Formatted in a clean, colour-coded A4 layout suitable for printing or tablet use at the clinic.
This is a shared conversation. Sign in to Orris to start your own chat.