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name: clif-icu description: Analyzes ICU clinical data using the Common Longitudinal ICU data Format (CLIF) and clifpy Python library. Loads and filters CLIF tables (vitals, labs, medications, respiratory support, microbiology) by hospitalization_id and category columns. Computes clinical scores including SOFA, Charlson Comorbidity Index (CCI), and Elixhauser. Creates wide datasets and performs data transformations. Use when working with ICU data, CLIF format, clifpy, clinical scoring, ventilator data, sepsis research, or intensive care analytics.

CLIF + clifpy

CLIF (Common Longitudinal ICU data Format) + clifpy Python library for ICU data analysis.

Copypip install clifpy

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When to Use This Skill

Activate this skill when:

• Working with ICU/intensive care unit clinical data
• Using or asking about the CLIF data format
• Loading data with the clifpy Python library
• Computing clinical scores (SOFA, CCI, Elixhauser, MDRO)
• Processing vitals, labs, medications, or respiratory support data
• Creating wide datasets from longitudinal ICU data
• Researching sepsis, ARDS, or critical care outcomes

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Instructions

Follow these steps when working with CLIF data:

1. Identify required tables - Determine which CLIF tables contain the data needed (vitals, labs, medications, etc.)
2. Always filter data - Use hospitalization_id filters on all tables; add category filters on long tables (see filtering rules below)
3. Choose the right approach:
   • Use individual table classes for most tasks (faster, more memory efficient)
   • Use ClifOrchestrator only when creating wide datasets or computing SOFA scores
4. Look up category values - Check mCIDE/ for valid category values before filtering
5. Compute clinical scores - Use ClifOrchestrator for SOFA, or refer to reference/clinical-scores.md for CCI and Elixhauser

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Critical: Always Filter Data

Long Tables (Use BOTH hospitalization_id AND category filters)

These tables have many rows per hospitalization. Always filter by category column:

Other Tables (hospitalization_id filter only, if needed)

All other tables (patient, hospitalization, adt, code_status, position, crrt_therapy, ecmo_mcs, hospital_diagnosis, patient_procedures, microbiology_susceptibility, microbiology_nonculture) have fewer rows per hospitalization. Filter by hospitalization_id only when needed.

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Quick Start

Load Individual Tables (Preferred)

Copyfrom clifpy.tables import Vitals, Labs, PatientAssessments

hosp_ids = ['H001', 'H002', 'H003']

# Always filter by BOTH hospitalization_id AND category
vitals = Vitals.from_file(
    data_directory='/path/to/data',
    filetype='parquet',
    timezone='US/Eastern',
    filters={
        'hospitalization_id': hosp_ids,
        'vital_category': ['heart_rate', 'sbp', 'spo2']
    }
)

labs = Labs.from_file(
    data_directory='/path/to/data',
    filetype='parquet',
    timezone='US/Eastern',
    filters={
        'hospitalization_id': hosp_ids,
        'lab_category': ['hemoglobin', 'creatinine', 'lactate']
    }
)

# Access DataFrames
vitals_df = vitals.df
labs_df = labs.df

ClifOrchestrator (Only for Wide Datasets)

Copyfrom clifpy import ClifOrchestrator

co = ClifOrchestrator(
    data_directory='/path/to/data',
    filetype='parquet',
    timezone='US/Eastern'
)
co.load_table('vitals', filters={'hospitalization_id': hosp_ids})

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Example Scripts

Complete workflow examples in scripts/:

cohort_identification_example.py

End-to-end cohort identification workflow:

1. Load core tables (patient, hospitalization, adt)
2. Filter adults (age >= 18) and date range (2018-2024)
3. Stitch encounters using 6-hour windows
4. Identify CRRT encounters
5. Exclude ESRD patients (ICD codes N185, N186, Z992)
6. Check weight data availability
7. Build final cohort with demographics
8. Save to parquet

Copyfrom clifpy.clif_orchestrator import ClifOrchestrator
from clifpy.utils.stitching_encounters import stitch_encounters

sofa_score_calculation.py

SOFA score computation workflow:

1. Load cohort and define time windows (e.g., first 24h)
2. Load required tables (labs, vitals, assessments, medications, respiratory)
3. Clean medication data (remove null doses)
4. Convert vasopressor units to mcg/kg/min
5. Create wide dataset with REQUIRED_SOFA_CATEGORIES_BY_TABLE
6. Compute 6 SOFA components (respiratory, coagulation, liver, cardiovascular, CNS, renal)
7. Save results to CSV

Copyfrom clifpy.clif_orchestrator import ClifOrchestrator
from clifpy.utils.sofa import REQUIRED_SOFA_CATEGORIES_BY_TABLE

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Reference Files

For detailed information, read the appropriate reference file:

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All Table Classes

Copyfrom clifpy.tables import (
    Patient, Hospitalization, Adt,
    Vitals, Labs, RespiratorySupport, Position,
    MedicationAdminContinuous, MedicationAdminIntermittent,
    PatientAssessments, HospitalDiagnosis,
    CodeStatus, CrrtTherapy, EcmoMcs,
    MicrobiologyCulture, MicrobiologyNonculture, MicrobiologySusceptibility,
    PatientProcedures
)

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Performance Rules

1. Use individual table classes - Not ClifOrchestrator
2. ALWAYS filter by hospitalization_id
3. Filter long tables by category
4. Use parquet - Faster than CSV
5. Limit columns - columns=['col1', 'col2']

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Requirements

Copypip install clifpy

• Python 3.8+
• Dependencies: pandas, pyarrow (for parquet support)