448 lines
19 KiB
Python
448 lines
19 KiB
Python
"""
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ICD-coded sepsis prevalence — inclusion vs exclusion cohort
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═════════════════════════════════════════════════════════════════════════════
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Companion analysis for paper3_phase5b_refined.py.
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The Phase 5b cohort SQL (`q_cohort()`) keeps an ICU stay only when ALL of:
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1. sepsis3.sepsis3 = TRUE (Sepsis-3 derived flag)
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2. ICU length-of-stay ≥ 24h (H_SNAPSHOT)
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3. sapsii IS NOT NULL AND sapsii ≥ 48 (SAPS-II Q4)
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This script computes the prevalence of *explicit* ICD-coded sepsis on:
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(a) the INCLUSION cohort — stays that satisfy all three filters,
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(b) the EXCLUSION cohort — every other ICU stay in MIMIC-III (fails
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at least one of the three filters above), and
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(c) ALL ICU STAYS — the full mimiciii.icustays universe
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(= inclusion ∪ exclusion).
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Note: stay-level totals partition cleanly (incl + excl = all), but at the
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admission and subject level a single hadm_id / subject can have ICU stays
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in both buckets, so the "all" row is computed via SQL GROUPING SETS rather
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than by summing.
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ICD-coded sepsis is evaluated at the hospital-admission level (a stay is
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"ICD-sepsis +" if its parent hadm_id carries any of the codes below):
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- Explicit sepsis (ICD-9): 995.91, 995.92, 785.52
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(matches paper2_festung_teil3.py SYNDROME_ICDS["sepsis"]["icd_9"])
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- Angus septicemia (ICD-9): 038.* (any 038-prefixed code)
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- Any of the above (union)
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For each cohort × definition combination we report:
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n positives, prevalence, Wilson 95% CI.
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The inclusion vs exclusion difference is reported with a normal-approx
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95% CI and a Pearson χ² statistic (no scipy dependency).
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Usage:
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python paper3_phase5b_icd_sepsis_prevalence.py
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"""
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import json, math, os, sys, time
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# Reuse the same env-var contract as paper3_phase5b_refined.py.
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PG_DSN = os.environ.get("MIMIC_PG_DSN", "dbname=mimic3")
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MIMIC_SCHEMA = os.environ.get("MIMIC_SCHEMA", "mimiciii")
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DERIVED_SCHEMA = os.environ.get("DERIVED_SCHEMA", MIMIC_SCHEMA)
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H_SNAPSHOT = 24 # ICU LOS threshold, hours (matches paper3 phase 5b)
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SAPSII_MIN = 48 # SAPS-II Q4 cutoff (matches paper3 phase 5b)
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OUT_FILE = "paper3_phase5b_icd_sepsis_prevalence.json"
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# Explicit sepsis codes (ICD-9, MIMIC-III stores them WITHOUT decimal point):
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# 995.91 → '99591' Sepsis
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# 995.92 → '99592' Severe sepsis
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# 785.52 → '78552' Septic shock
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EXPLICIT_SEPSIS_ICD9 = ("99591", "99592", "78552")
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# Angus-style broad septicemia bucket: any ICD-9 starting with 038.
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SEPTICEMIA_PREFIX = "038"
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_PG_CONN = None
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def _pg_conn():
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global _PG_CONN
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if _PG_CONN is None or getattr(_PG_CONN, "closed", 0):
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import psycopg2
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_PG_CONN = psycopg2.connect(PG_DSN)
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_PG_CONN.set_session(readonly=True, autocommit=True)
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return _PG_CONN
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def run_pg(sql, label=""):
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import psycopg2.extras
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conn = _pg_conn()
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t0 = time.time()
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with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
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cur.execute(sql)
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rows = [dict(r) for r in cur.fetchall()] if cur.description else []
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print(f" {label:40s} {len(rows):>8,d} rows ({time.time()-t0:.1f}s)")
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return rows
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# ── SQL ─────────────────────────────────────────────────────────────────────
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#
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# One pass: classify every ICU stay as "inclusion" or "exclusion" using the
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# Phase 5b filter, then left-join the two ICD code sets at the hadm_id level
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# and aggregate. This mirrors q_cohort() exactly for the inclusion bucket
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# (sepsis3 = TRUE AND LOS ≥ 24h AND sapsii ≥ 48), and treats every other
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# ICU stay in mimiciii.icustays as exclusion. GROUPING SETS adds a third
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# row (cohort = NULL → 'all') aggregated over the full ICU universe so that
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# admission- and subject-level distinct counts are correct (a single hadm_id
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# may straddle both buckets, so we cannot just sum incl + excl).
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def q_prevalence():
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explicit = ",".join(f"'{c}'" for c in EXPLICIT_SEPSIS_ICD9)
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return f"""
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WITH icu AS (
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SELECT icu.icustay_id,
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icu.hadm_id,
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icu.subject_id,
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EXTRACT(EPOCH FROM (icu.outtime - icu.intime)) / 3600.0 AS los_h,
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COALESCE(s3.sepsis3, FALSE) AS is_sepsis3,
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saps.sapsii AS sapsii
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FROM {MIMIC_SCHEMA}.icustays icu
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LEFT JOIN {DERIVED_SCHEMA}.sepsis3 s3 ON s3.icustay_id = icu.icustay_id
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LEFT JOIN {DERIVED_SCHEMA}.sapsii saps ON saps.icustay_id = icu.icustay_id
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WHERE icu.icustay_id IS NOT NULL
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AND icu.hadm_id IS NOT NULL
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),
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classified AS (
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SELECT icustay_id, hadm_id, subject_id,
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is_sepsis3, los_h, sapsii,
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CASE WHEN is_sepsis3 = TRUE
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AND los_h >= {H_SNAPSHOT}
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AND sapsii IS NOT NULL
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AND sapsii >= {SAPSII_MIN}
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THEN 'inclusion' ELSE 'exclusion' END AS cohort
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FROM icu
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),
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explicit_sepsis AS (
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SELECT DISTINCT hadm_id
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FROM {MIMIC_SCHEMA}.diagnoses_icd
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WHERE icd9_code IN ({explicit})
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),
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septicemia AS (
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SELECT DISTINCT hadm_id
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FROM {MIMIC_SCHEMA}.diagnoses_icd
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WHERE icd9_code LIKE '{SEPTICEMIA_PREFIX}%%'
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)
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SELECT
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COALESCE(c.cohort, 'all') AS cohort,
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COUNT(*) AS n_stays,
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COUNT(DISTINCT c.hadm_id) AS n_admissions,
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COUNT(DISTINCT c.subject_id) AS n_subjects,
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SUM(CASE WHEN e.hadm_id IS NOT NULL
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THEN 1 ELSE 0 END) AS n_stays_explicit,
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COUNT(DISTINCT CASE WHEN e.hadm_id IS NOT NULL
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THEN c.hadm_id END) AS n_adm_explicit,
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SUM(CASE WHEN s.hadm_id IS NOT NULL
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THEN 1 ELSE 0 END) AS n_stays_septicemia,
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COUNT(DISTINCT CASE WHEN s.hadm_id IS NOT NULL
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THEN c.hadm_id END) AS n_adm_septicemia,
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SUM(CASE WHEN e.hadm_id IS NOT NULL
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OR s.hadm_id IS NOT NULL
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THEN 1 ELSE 0 END) AS n_stays_any,
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COUNT(DISTINCT CASE WHEN e.hadm_id IS NOT NULL
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OR s.hadm_id IS NOT NULL
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THEN c.hadm_id END) AS n_adm_any
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FROM classified c
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LEFT JOIN explicit_sepsis e ON e.hadm_id = c.hadm_id
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LEFT JOIN septicemia s ON s.hadm_id = c.hadm_id
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GROUP BY GROUPING SETS ((c.cohort), ())
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"""
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def q_exclusion_breakdown():
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"""How many excluded stays fail each individual filter (non-exclusive
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counts; an excluded stay can violate >1 criterion)."""
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return f"""
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WITH icu AS (
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SELECT icu.icustay_id,
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EXTRACT(EPOCH FROM (icu.outtime - icu.intime)) / 3600.0 AS los_h,
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COALESCE(s3.sepsis3, FALSE) AS is_sepsis3,
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saps.sapsii AS sapsii
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FROM {MIMIC_SCHEMA}.icustays icu
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LEFT JOIN {DERIVED_SCHEMA}.sepsis3 s3 ON s3.icustay_id = icu.icustay_id
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LEFT JOIN {DERIVED_SCHEMA}.sapsii saps ON saps.icustay_id = icu.icustay_id
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WHERE icu.icustay_id IS NOT NULL
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AND icu.hadm_id IS NOT NULL
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)
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SELECT
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COUNT(*) AS n_total,
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SUM(CASE WHEN is_sepsis3 = FALSE
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THEN 1 ELSE 0 END) AS n_not_sepsis3,
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SUM(CASE WHEN los_h < {H_SNAPSHOT}
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THEN 1 ELSE 0 END) AS n_los_short,
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SUM(CASE WHEN sapsii IS NULL
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THEN 1 ELSE 0 END) AS n_sapsii_null,
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SUM(CASE WHEN sapsii IS NOT NULL AND sapsii < {SAPSII_MIN}
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THEN 1 ELSE 0 END) AS n_sapsii_below
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FROM icu
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"""
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def q_icd_sepsis_waterfall():
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"""Mutually-exclusive waterfall, restricted to ICD-coded sepsis stays
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only, showing which inclusion gate eliminated them. Uses the union
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definition (explicit sepsis codes ∪ 038.* septicemia)."""
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explicit = ",".join(f"'{c}'" for c in EXPLICIT_SEPSIS_ICD9)
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return f"""
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WITH icu AS (
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SELECT icu.icustay_id, icu.hadm_id,
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EXTRACT(EPOCH FROM (icu.outtime - icu.intime)) / 3600.0 AS los_h,
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COALESCE(s3.sepsis3, FALSE) AS is_sepsis3,
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saps.sapsii AS sapsii
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FROM {MIMIC_SCHEMA}.icustays icu
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LEFT JOIN {DERIVED_SCHEMA}.sepsis3 s3 ON s3.icustay_id = icu.icustay_id
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LEFT JOIN {DERIVED_SCHEMA}.sapsii saps ON saps.icustay_id = icu.icustay_id
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WHERE icu.icustay_id IS NOT NULL
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AND icu.hadm_id IS NOT NULL
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),
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icd_pos AS (
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SELECT DISTINCT hadm_id
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FROM {MIMIC_SCHEMA}.diagnoses_icd
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WHERE icd9_code IN ({explicit})
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OR icd9_code LIKE '{SEPTICEMIA_PREFIX}%%'
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),
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icd_stays AS (
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SELECT i.* FROM icu i JOIN icd_pos x ON x.hadm_id = i.hadm_id
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)
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SELECT
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COUNT(*) AS n_total_stays,
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COUNT(DISTINCT hadm_id) AS n_total_adm,
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-- Waterfall: each stay is counted exactly once, in the order of the
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-- inclusion filter (sepsis3 → LOS → sapsii NULL → sapsii < 48 → pass).
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SUM(CASE WHEN NOT is_sepsis3
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THEN 1 ELSE 0 END) AS n_fail_sepsis3,
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SUM(CASE WHEN is_sepsis3 AND los_h < {H_SNAPSHOT}
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THEN 1 ELSE 0 END) AS n_fail_los,
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SUM(CASE WHEN is_sepsis3 AND los_h >= {H_SNAPSHOT}
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AND sapsii IS NULL
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THEN 1 ELSE 0 END) AS n_fail_sapsii_null,
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SUM(CASE WHEN is_sepsis3 AND los_h >= {H_SNAPSHOT}
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AND sapsii IS NOT NULL
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AND sapsii < {SAPSII_MIN}
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THEN 1 ELSE 0 END) AS n_fail_sapsii_below,
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SUM(CASE WHEN is_sepsis3 AND los_h >= {H_SNAPSHOT}
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AND sapsii IS NOT NULL
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AND sapsii >= {SAPSII_MIN}
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THEN 1 ELSE 0 END) AS n_pass
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FROM icd_stays
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"""
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# ── Stats helpers (no scipy) ────────────────────────────────────────────────
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def wilson_ci(k, n, z=1.959963984540054):
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"""Wilson score 95% CI for a binomial proportion. Returns (lo, hi)."""
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if n <= 0:
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return (float("nan"), float("nan"))
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p = k / n
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denom = 1.0 + z*z/n
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centre = (p + z*z/(2.0*n)) / denom
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half = (z * math.sqrt(p*(1.0 - p)/n + z*z/(4.0*n*n))) / denom
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return (max(0.0, centre - half), min(1.0, centre + half))
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def diff_ci(k1, n1, k2, n2, z=1.959963984540054):
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"""Normal-approx 95% CI for (p1 − p2). Returns (delta, lo, hi)."""
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if n1 <= 0 or n2 <= 0:
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return (float("nan"),) * 3
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p1, p2 = k1 / n1, k2 / n2
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se = math.sqrt(p1*(1.0 - p1)/n1 + p2*(1.0 - p2)/n2)
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d = p1 - p2
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return (d, d - z*se, d + z*se)
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def chi2_2x2(k1, n1, k2, n2):
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"""Pearson χ² for the 2×2 table (sepsis± × cohort). Returns (chi2, dof=1).
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Critical value at p=0.05 is 3.841."""
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a, b = k1, n1 - k1 # incl: sepsis+, sepsis−
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c, d = k2, n2 - k2 # excl: sepsis+, sepsis−
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n = n1 + n2
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if n == 0: return (float("nan"), 1)
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row1, row2 = a + b, c + d
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col1, col2 = a + c, b + d
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chi2 = 0.0
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for obs, r, col in ((a, row1, col1), (b, row1, col2),
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(c, row2, col1), (d, row2, col2)):
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exp = r * col / n
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if exp > 0:
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chi2 += (obs - exp) ** 2 / exp
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return (chi2, 1)
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def fmt_pct(p): return f"{100.0*p:5.2f}%"
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def fmt_ci(lo,hi): return f"[{100.0*lo:5.2f}, {100.0*hi:5.2f}]"
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# ── Main ────────────────────────────────────────────────────────────────────
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def main():
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print("\n" + "█"*78)
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print(" ICD-coded sepsis prevalence — Phase 5b inclusion vs exclusion")
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print("█"*78)
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print(f"\n PG DSN: {PG_DSN}")
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print(f" MIMIC schema: {MIMIC_SCHEMA}")
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print(f" Derived schema: {DERIVED_SCHEMA}")
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print(f" Inclusion: sepsis3=TRUE AND LOS≥{H_SNAPSHOT}h AND SAPS-II≥{SAPSII_MIN}")
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print(f" Explicit ICD-9: {', '.join(EXPLICIT_SEPSIS_ICD9)} "
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f"(995.91 / 995.92 / 785.52)")
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print(f" Septicemia: ICD-9 {SEPTICEMIA_PREFIX}.*")
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print(f"\n[1] Querying MIMIC-III...")
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rows = run_pg(q_prevalence(), "cohort × ICD prevalence")
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bkdwn = run_pg(q_exclusion_breakdown(), "exclusion breakdown")
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wfall = run_pg(q_icd_sepsis_waterfall(), "ICD-sepsis waterfall")
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if not rows:
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print("\n[ERROR] no rows returned. Check PG_DSN / schema permissions.")
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sys.exit(1)
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by = {r["cohort"]: r for r in rows}
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incl = by.get("inclusion", {})
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excl = by.get("exclusion", {})
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allc = by.get("all", {})
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INT_KEYS = ("n_stays","n_admissions","n_subjects",
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"n_stays_explicit","n_adm_explicit",
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"n_stays_septicemia","n_adm_septicemia",
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"n_stays_any","n_adm_any")
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for c in (incl, excl, allc):
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for k in INT_KEYS:
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c[k] = int(c.get(k) or 0)
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COHORTS = (("inclusion", incl), ("exclusion", excl), ("all", allc))
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# ── [2] Cohort sizes ──────────────────────────────────────────────────
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print(f"\n[2] Cohort sizes")
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print(f" {'cohort':12s} {'stays':>8s} {'admissions':>11s} {'subjects':>9s}")
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for label, c in COHORTS:
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print(f" {label:12s} {c['n_stays']:>8,d} "
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f"{c['n_admissions']:>11,d} {c['n_subjects']:>9,d}")
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# ── [3] Why an ICU stay was excluded ──────────────────────────────────
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if bkdwn:
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b = bkdwn[0]
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print(f"\n[3] Exclusion breakdown (non-exclusive: a stay can fail >1 filter)")
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n_total = int(b['n_total'] or 0)
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for lbl, k in (("not Sepsis-3", "n_not_sepsis3"),
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(f"ICU LOS < {H_SNAPSHOT}h", "n_los_short"),
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("SAPS-II is NULL", "n_sapsii_null"),
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(f"SAPS-II < {SAPSII_MIN}", "n_sapsii_below")):
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n = int(b[k] or 0)
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pct = 100.0*n/n_total if n_total else 0.0
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print(f" {lbl:24s} {n:>8,d} ({pct:5.2f}% of all ICU stays)")
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print(f" {'all ICU stays':24s} {n_total:>8,d}")
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# ── [3b] ICD-sepsis-positive waterfall ────────────────────────────────
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# Diagnostic: of the ICD-coded sepsis stays (explicit ∪ 038.*), which
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# inclusion gate eliminated them? Mutually exclusive: each stay is
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# counted in the FIRST gate that would reject it, walking in the
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# inclusion-filter order (sepsis3 → LOS → SAPS-II NULL → SAPS-II<48).
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if wfall:
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w = wfall[0]
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n_w = int(w["n_total_stays"] or 0)
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n_adm = int(w["n_total_adm"] or 0)
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print(f"\n[3b] ICD-sepsis-positive waterfall (mutually exclusive,"
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f" inclusion-filter order)")
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print(f" {'gate':28s} {'n stays':>9s} {'pct':>6s} cumulative")
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cum = 0
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steps = (
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("rejected: not Sepsis-3", "n_fail_sepsis3"),
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(f"rejected: LOS < {H_SNAPSHOT}h", "n_fail_los"),
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("rejected: SAPS-II is NULL", "n_fail_sapsii_null"),
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(f"rejected: SAPS-II < {SAPSII_MIN}", "n_fail_sapsii_below"),
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(f"PASS (= inclusion)", "n_pass"),
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)
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for lbl, kn in steps:
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n = int(w[kn] or 0)
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cum += n
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pct = 100.0*n/n_w if n_w else 0.0
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print(f" {lbl:28s} {n:>9,d} {pct:5.2f}% {cum:>9,d}")
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print(f" {'TOTAL ICD-sepsis stays':28s} {n_w:>9,d} "
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f"({n_adm:,} admissions)")
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# ── [4] Prevalence per definition ─────────────────────────────────────
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DEFS = (
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("Explicit sepsis (995.91 / 995.92 / 785.52)",
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"n_stays_explicit", "n_adm_explicit"),
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("Angus septicemia (038.*)",
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"n_stays_septicemia", "n_adm_septicemia"),
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("Any of the above (union)",
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"n_stays_any", "n_adm_any"),
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)
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def _table(title, denom_key_n, denom_key_k):
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"""Render the prevalence table for one denominator (stays or
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admissions) and append rows to `results[bucket]`."""
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print(f"\n{title}")
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print(f" {'definition':45s} {'cohort':10s} "
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f"{'n+':>7s} {'N':>7s} {'prev':>7s} {'95% CI (Wilson)':>18s}")
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out = []
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for name, sk, ak in DEFS:
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kkey = sk if denom_key_k == "n_stays" else ak
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for label, c in COHORTS:
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k_, n_ = c[kkey], c[denom_key_n]
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p = k_/n_ if n_ else float("nan")
|
||
lo, hi = wilson_ci(k_, n_)
|
||
print(f" {name:45s} {label:10s} "
|
||
f"{k_:>7,d} {n_:>7,d} {fmt_pct(p):>7s} "
|
||
f"{fmt_ci(lo,hi):>18s}")
|
||
out.append({"definition": name, "cohort": label,
|
||
"k": k_, "n": n_, "prevalence": p,
|
||
"ci_lo": lo, "ci_hi": hi})
|
||
|
||
# Inclusion vs exclusion comparison (the "all" row is just a
|
||
# weighted average of the two so a Δ against it isn't meaningful).
|
||
k1, n1 = incl[kkey], incl[denom_key_n]
|
||
k2, n2 = excl[kkey], excl[denom_key_n]
|
||
d, dlo, dhi = diff_ci(k1, n1, k2, n2)
|
||
chi2, dof = chi2_2x2(k1, n1, k2, n2)
|
||
sig = "p<0.05" if chi2 > 3.841 else "n.s."
|
||
print(f" {' Δ (incl − excl)':45s} {'':10s} "
|
||
f"{'':>7s} {'':>7s} {fmt_pct(d):>7s} "
|
||
f"{fmt_ci(dlo,dhi):>18s} χ²={chi2:6.2f} ({sig})")
|
||
out.append({"definition": name, "cohort": "delta_incl_minus_excl",
|
||
"delta": d, "ci_lo": dlo, "ci_hi": dhi,
|
||
"chi2": chi2, "dof": dof})
|
||
return out
|
||
|
||
results = {
|
||
"by_stays": _table("[4] ICD-coded sepsis prevalence (denominator = ICU STAYS)",
|
||
"n_stays", "n_stays"),
|
||
"by_admissions": _table("[5] ICD-coded sepsis prevalence (denominator = ADMISSIONS)",
|
||
"n_admissions", "n_admissions"),
|
||
}
|
||
|
||
# ── Save ────────────────────────────────────────────────────────────
|
||
output = {
|
||
"filters": {
|
||
"h_snapshot_hours": H_SNAPSHOT,
|
||
"sapsii_min": SAPSII_MIN,
|
||
"explicit_icd9": list(EXPLICIT_SEPSIS_ICD9),
|
||
"septicemia_prefix": SEPTICEMIA_PREFIX,
|
||
},
|
||
"cohorts": {
|
||
"inclusion": {k: incl[k] for k in INT_KEYS},
|
||
"exclusion": {k: excl[k] for k in INT_KEYS},
|
||
"all": {k: allc[k] for k in INT_KEYS},
|
||
},
|
||
"exclusion_breakdown": (bkdwn[0] if bkdwn else None),
|
||
"icd_sepsis_waterfall": (wfall[0] if wfall else None),
|
||
"results": results,
|
||
}
|
||
with open(OUT_FILE, "w") as f:
|
||
json.dump(output, f, indent=2, default=str)
|
||
print(f"\n → Saved: {OUT_FILE}")
|
||
print("\n" + "█"*78 + "\n")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|