willemjan
b8e01fa8c9
Dutch lab results conventionally report HbA1c in mmol/mol rather than DCCT/NGSP %. Adds gmi_mmol_per_mol() using the standard IFCC conversion (Hoelzel et al. 2004) and shows it next to the existing % value on the Samenvatting page and in the PDF summary. Closes gitea #25.
87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
import requests
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import streamlit as st
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from auth import require_auth
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from nightscout_client import NightscoutConfigError
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from reporting import (
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gmi_mmol_per_mol,
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gmi_percent,
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percent_cv,
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sensor_active_percent,
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tir_breakdown_5band,
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)
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from ui_common import (
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build_tir_stacked_bar_figure,
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get_colors,
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load_range_data,
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render_completeness_warning,
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select_period_days,
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)
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st.set_page_config(page_title="Glucose dashboard — Samenvatting", page_icon="📊", layout="wide")
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require_auth()
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st.title("Samenvatting")
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st.caption("AGP-stijl samenvattingsstatistieken over een gekozen periode (FR2, FR9).")
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start, end, days = select_period_days(default_label="90 dagen")
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try:
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glucose, _treatments = load_range_data(start, end)
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except NightscoutConfigError as exc:
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st.error(str(exc))
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st.stop()
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except (requests.RequestException, ValueError) as exc:
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st.error(f"Kon geen data ophalen van Nightscout: {exc}")
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st.stop()
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if glucose.empty:
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st.info("Geen glucosemetingen gevonden in deze periode.")
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st.stop()
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render_completeness_warning(glucose, start, end)
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active_pct = sensor_active_percent(glucose, start, end)
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mean_mmol = glucose["mmol"].mean()
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gmi = gmi_percent(mean_mmol)
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gmi_mmol_mol = gmi_mmol_per_mol(gmi)
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cv = percent_cv(glucose)
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tir = tir_breakdown_5band(glucose)
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st.subheader("Kerncijfers")
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col1, col2, col3, col4 = st.columns(4)
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col1.metric("Sensordekking", f"{active_pct:.0f}%", help="Streefwaarde: ≥70%")
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col2.metric("Gemiddelde glucose", f"{mean_mmol:.1f} mmol/L")
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col3.metric(
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"Geschat HbA1c (GMI)",
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f"{gmi:.1f}% ({gmi_mmol_mol:.0f} mmol/mol)",
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help="Bergenstal et al. 2018 — geen labwaarde",
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)
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col4.metric("Variabiliteit (%CV)", f"{cv:.0f}%", help="Streefwaarde: ≤36%")
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st.subheader("Tijd in bereik (5 klinische banden)")
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band_labels = {
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"very_low": "Zeer laag (<3.0)",
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"low": "Laag (3.0-3.9)",
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"in_range": "In bereik (3.9-10.0)",
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"high": "Hoog (10.0-13.9)",
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"very_high": "Zeer hoog (>13.9)",
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}
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band_targets = {
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"very_low": "<1%",
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"low": "<4% (samen met zeer laag)",
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"in_range": ">70%",
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"high": "<25% (samen met zeer hoog)",
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"very_high": "<5%",
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}
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cols = st.columns(5)
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for col, band in zip(cols, band_labels, strict=True):
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col.metric(band_labels[band], f"{tir[band]:.0f}%", help=f"Streefwaarde: {band_targets[band]}")
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st.plotly_chart(build_tir_stacked_bar_figure(tir, get_colors()), width="stretch")
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st.caption(
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f"Periode: {start.strftime('%d-%m-%Y')} t/m {end.strftime('%d-%m-%Y')} "
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f"({len(glucose)} metingen) · streefwaarden volgens Battelino et al. 2019"
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)
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