HealthLog/src/lib/analytics/bp-grade.ts

/**
 * v1.15.12 A1 — graded blood-pressure pillar score.
 *
 * The Personal Health Score's BP pillar previously equalled
 * `bpInTargetRate`: the share of paired readings where BOTH systolic and
 * diastolic sat at-or-below the age-band ceiling, scored binary per
 * reading over the all-time window. That metric is honest as a "% of
 * readings in range" stat, but it is the wrong thing to use AS the
 * pillar score: a user who averages 134/87 (borderline stage-1, every
 * reading slightly over the diastolic ceiling) collapses to ~10-16/100
 * because nearly every reading fails the binary check — even though
 * clinically they are "a bit high", not catastrophic. Improvement also
 * never shows, because the rate is all-time and a binary cliff.
 *
 * This module replaces the binary rate AS THE SCORE with a smooth
 * clinical-proximity grade. A representative BP (recency-weighted toward
 * the last weeks) is mapped to a 0-100 score per axis, and the pillar
 * takes the WORSE of the two sub-scores — so an elevated diastolic
 * counts against the score (it should) without binary-zeroing it.
 *
 * The curve is anchored on the SAME age-band ceiling the rest of the app
 * uses (`BpTargets.sysHigh` / `diaHigh` from `bp-targets.ts`). No new
 * thresholds are invented; the score bands are derived as offsets from
 * the configured ceiling:
 *
 *   offset from ceiling   →  axis score
 *   ≤ −12 (well below)        100  (optimal — comfortably in band)
 *      0  (at the ceiling)     85  (at goal — "well controlled")
 *     +5                       65
 *    +10                       52
 *    +20                       40
 *    +40                       18
 *    ≥ +60                      5  (far above — uncontrolled)
 *
 * Piecewise-linear between anchors — no hard cliffs, so a one-mmHg
 * change moves the score by a small, continuous amount. The diastolic
 * and systolic axes share the same offset→score table, so each axis is
 * judged against its own ceiling.
 *
 * Hypotension is penalised on a SEPARATE under-floor curve that is
 * CONTINUOUS with the optimal plateau at the boundary: at exactly the
 * clinical floor (`sys = 90`, `dia = 50`, mirroring `isBpReadingInTarget`)
 * the score is 100, and it descends smoothly as the value drops further
 * below the floor. The earlier implementation fed `floor − value` through
 * the over-target table, which made 1 mmHg under the floor jump to ~81 —
 * a 19-point cliff against the 100 the plateau holds AT the floor. The
 * dedicated anchors below (floor→100, floor−10→70, floor−20→45,
 * floor−30→20) mirror the over-target steepness without the cliff, so the
 * curve is continuous on BOTH sides of every boundary.
 *
 * Pure & deterministic — fully unit-tested in `__tests__/bp-grade.test.ts`.
 */
import type { BpTargets } from "./bp-targets";
import { SYS_HYPOTENSION_FLOOR, DIA_HYPOTENSION_FLOOR } from "./bp-in-target";

/**
 * Offset-from-ceiling → axis-score anchors. Offsets are in mmHg; the
 * first anchor is the "optimal" plateau (comfortably below the ceiling),
 * the last is the "uncontrolled" floor. Interpolated piecewise-linearly.
 */
const AXIS_ANCHORS: ReadonlyArray<readonly [offset: number, score: number]> = [
  [-12, 100],
  [0, 85],
  [5, 65],
  [10, 52],
  [20, 40],
  [40, 18],
  [60, 5],
] as const;

/**
 * Below-floor hypotension anchors. `below` is mmHg the value sits UNDER
 * the clinical floor; the score starts at 100 AT the floor (`below = 0`)
 * so the curve is continuous with the optimal plateau the over-target
 * branch holds at the floor, then descends to mirror the over-target
 * steepness. Interpolated piecewise-linearly; clamps below the last
 * anchor.
 */
const HYPO_ANCHORS: ReadonlyArray<readonly [below: number, score: number]> = [
  [0, 100],
  [10, 70],
  [20, 45],
  [30, 20],
  [40, 5],
] as const;

/**
 * Which axis set the score, measured against which boundary, and by how
 * far. Published with the score (never beside it) so the surface can
 * answer "why this number" without recomputing anything client-side.
 *
 * `relation` is a trichotomy on the binding axis' own value:
 *
 *   value <  floor                → `below_floor`   (boundary = floor)
 *   value <= ceiling              → `in_band`       (boundary = ceiling)
 *   otherwise                     → `above_ceiling` (boundary = ceiling)
 *
 * so the three relations are exhaustive and mutually exclusive by
 * construction, whatever the anchors say. `offsetMmHg` is the distance
 * from that boundary as a NON-NEGATIVE integer (rounded); the relation
 * carries the direction, so the number never needs a sign. The relation
 * is decided on the unrounded value, so it always agrees with the score
 * even when the rounded offset reads 0.
 */
export type BpScoreAxis = "systolic" | "diastolic";
export type BpScoreRelation = "above_ceiling" | "below_floor" | "in_band";

export interface BpScoreBasis {
  /** The axis whose sub-score the pillar took — the worse of the two. */
  axis: BpScoreAxis;
  relation: BpScoreRelation;
  /** Rounded, non-negative distance from `boundaryMmHg`. */
  offsetMmHg: number;
  /** The ceiling the axis was graded against, or its hypotension floor. */
  boundaryMmHg: number;
}

/**
 * A graded BP reading: the score AND the basis that produced it, as one
 * value. They are returned together, and carried together through every
 * envelope down to the pillar, precisely so no caller can ever pair a
 * score from one run with a basis from another (different representative
 * pair, or — the live trap — different targets).
 */
export interface BpGrade {
  score: number;
  basis: BpScoreBasis;
}

/**
 * Generic piecewise-linear interpolation over a monotonic-x anchor table.
 * Clamps to the first / last anchor outside the table range.
 */
function interpolateAnchors(
  anchors: ReadonlyArray<readonly [number, number]>,
  x: number,
): number {
  const first = anchors[0];
  const last = anchors[anchors.length - 1];
  if (x <= first[0]) return first[1];
  if (x >= last[0]) return last[1];
  for (let i = 0; i < anchors.length - 1; i++) {
    const [x1, y1] = anchors[i];
    const [x2, y2] = anchors[i + 1];
    if (x >= x1 && x <= x2) {
      const fraction = (x - x1) / (x2 - x1);
      return y1 + fraction * (y2 - y1);
    }
  }
  return last[1];
}

/**
 * Piecewise-linear interpolation of an over-/under-target `offset`
 * (mmHg relative to the axis ceiling) onto the 0-100 axis score.
 * Clamps to the first / last anchor outside the table range.
 */
function interpolateAxisScore(offset: number): number {
  return interpolateAnchors(AXIS_ANCHORS, offset);
}

/**
 * Score one BP axis against its ceiling.
 *
 * - Above the floor: offset = `value - ceiling`. Negative offsets sit in
 *   the optimal plateau, positive ones climb the over-target curve.
 * - Below the hypotension floor: the distance below the floor is fed
 *   through the dedicated `HYPO_ANCHORS` curve, which starts at 100 AT
 *   the floor (`below = 0`) so the score is continuous with the plateau
 *   the over-target branch holds at the floor, then descends smoothly —
 *   no boundary cliff.
 */
function gradeAxis(
  value: number,
  ceiling: number,
  floor: number,
): { score: number; relation: BpScoreRelation; boundary: number } {
  if (value < floor) {
    return {
      score: interpolateAnchors(HYPO_ANCHORS, floor - value),
      relation: "below_floor",
      boundary: floor,
    };
  }
  const offset = value - ceiling;
  return {
    score: interpolateAxisScore(offset),
    relation: offset > 0 ? "above_ceiling" : "in_band",
    boundary: ceiling,
  };
}

/**
 * Map a representative blood-pressure reading to a smooth 0-100 score,
 * together with the basis that produced it.
 *
 * Takes the WORSE of the systolic and diastolic axis sub-scores so a
 * high diastolic counts against the score (it should) without
 * binary-zeroing the result the way the all-time in-target rate did.
 *
 * **Tie rule**: when both axes grade identically, the basis names the
 * SYSTOLIC axis. The two are interchangeable for the number, so the
 * choice is arbitrary in arithmetic but must not be arbitrary in the
 * text; systolic is the axis clinicians lead with and the one the app
 * shows first everywhere else.
 *
 * @returns integer score 0-100 plus the binding axis, its relation to
 *          its boundary, and the distance to it.
 */
export function gradeBpScore(input: {
  sys: number;
  dia: number;
  target: BpTargets;
}): BpGrade {
  const sys = gradeAxis(input.sys, input.target.sysHigh, SYS_HYPOTENSION_FLOOR);
  const dia = gradeAxis(input.dia, input.target.diaHigh, DIA_HYPOTENSION_FLOOR);
  // `<=` puts a tie on systolic — see the tie rule above.
  const bindingIsSystolic = sys.score <= dia.score;
  const binding = bindingIsSystolic ? sys : dia;
  const bindingValue = bindingIsSystolic ? input.sys : input.dia;
  return {
    score: Math.round(Math.max(0, Math.min(100, binding.score))),
    basis: {
      axis: bindingIsSystolic ? "systolic" : "diastolic",
      relation: binding.relation,
      offsetMmHg: Math.round(Math.abs(bindingValue - binding.boundary)),
      boundaryMmHg: binding.boundary,
    },
  };
}

/**
 * One timestamped BP pair for the recency-weighted representative.
 */
export interface BpPairPoint {
  /** Wall-clock of the reading (ms epoch ordering only — no tz math). */
  at: Date;
  sys: number;
  dia: number;
  /**
   * v1.15.12 — how many underlying readings this point stands for.
   * Defaults to 1 (one per-event pair). The rollup path collapses a day
   * into a single per-day-MEAN pair and passes `perDayPairCount` here so
   * a 4-reading day's mean counts 4× in the recency-weighted grade,
   * matching the live per-event behaviour. Without it a high-variance
   * day weighed 1:1 on rollup but N:1 on live, diverging the BP pillar by
   * up to ~20 points for the same data. The recency weight MULTIPLIES
   * this count.
   */
  count?: number;
}

/**
 * Collapse a series of paired BP readings into a single recency-weighted
 * representative `{ sys, dia }`, then grade it.
 *
 * Recent readings should dominate so that genuine improvement surfaces in
 * the score rather than being diluted by years of older readings. Each
 * pair is weighted by an exponential decay on its age relative to `now`,
 * with a half-life of `HALF_LIFE_DAYS` (45 d): a reading 45 days old
 * counts half as much as today's, 90 days old a quarter, etc. The
 * weighted mean of sys and dia is the representative reading.
 *
 * Returns `null` when the series is empty (caller leaves the pillar
 * absent — the redistribution helper then drops the BP weight cleanly).
 */
const HALF_LIFE_DAYS = 45;
const DAY_MS = 24 * 60 * 60 * 1000;

export function representativeBpFromSeries(input: {
  pairs: ReadonlyArray<BpPairPoint>;
  now: Date;
}): { sys: number; dia: number } | null {
  const { pairs, now } = input;
  if (pairs.length === 0) return null;

  const nowMs = now.getTime();
  let weightSum = 0;
  let sysWeighted = 0;
  let diaWeighted = 0;
  for (const p of pairs) {
    const ageDays = Math.max(0, (nowMs - p.at.getTime()) / DAY_MS);
    // The recency decay multiplies the per-point reading count so a
    // per-day-mean pair (rollup path) carries the same weight as the N
    // per-event pairs it stands for (live path).
    const count = p.count ?? 1;
    const weight = Math.pow(0.5, ageDays / HALF_LIFE_DAYS) * count;
    weightSum += weight;
    sysWeighted += weight * p.sys;
    diaWeighted += weight * p.dia;
  }
  if (weightSum === 0) return null;
  return {
    sys: sysWeighted / weightSum,
    dia: diaWeighted / weightSum,
  };
}

/**
 * Grade the recency-weighted representative of a pair series.
 *
 * Returns the score and its basis as ONE value. Callers must carry that
 * value on, never split it: a score stored beside a basis that came from
 * a second evaluation (or from a second set of targets — the analytics
 * route deliberately re-runs this with the CLINICAL targets when the user
 * has a personal one) would let the surface explain a number that is not
 * the number on screen.
 */
export function gradeBpScoreFromSeries(input: {
  pairs: ReadonlyArray<BpPairPoint>;
  target: BpTargets;
  now: Date;
}): BpGrade | null {
  const representative = representativeBpFromSeries(input);
  return representative
    ? gradeBpScore({ ...representative, target: input.target })
    : null;
}