Multivariate severity assessment and humane endpoint prediction for laboratory animal studies using the RELSA (RELative Severity Assessment) score and ARIMA-based foRcast forecasting. Use when combining welfare readouts — body weight or weight loss, body temperature, clinical or nesting scores, biom

RELSA severity assessment and humane endpoint forecasting Overview Severity assessment in animal research is legally mandatory and scientifically load bearing: it drives humane endpoint decisions, and poor welfare monitoring degrades reproducibility. The usual practice evaluates each readout in isolation — weight loss here, a clinical score there — which makes it hard to say how badly an individual animal is actually doing. This skill implements two published procedures that address that: RELSA (Talbot et al., 2022) combines several outcome measures into one score per animal per time point, expressed relative to a reference set of known burden . RELSA = 0 is baseline; RELSA = 1 means the animal has reached the reference set's maximum deviation. foRcast (Lutscher et al., 2026) fits an ARIMA model to an individual animal's RELSA trajectory and forecasts the next score with a 95% prediction interval, so animals heading for a humane endpoint can be identified before they get there. Kernel density estimation on the RELSA scale supplies candidate attention and danger zones for interpretation. The point is refinement : give at risk animals attention earlier, and avoid euthanising animals that would have recovered. Both procedures are aids to severity assessment, not decision rules — see [Boundaries]( boundaries state these when you report). When to use this skill Combining weight loss, temperature, clinical scoring, biomarkers, or telemetry into a single per animal severity score Asking which animals in a cohort are at risk of reaching a humane endpoint, or predicting the severity score at a coming time point Comparing severity between treatment groups, interventions, or animal models on a common relative scale Defining thresholds or zones on a severity scale from the data Writing the severity assessment section of an animal welfare report, a 3Rs/refinement analysis, or an application under EU Directive 2010/63/EU For general forecasting of a time series that is not a severity score, use timesfm forecasting or statsmodels . For study design and sample size, use experimental design and statistical power . Installation relsa score.py and kde thresholds.py need only numpy/pandas/scipy; statsmodels is required for forecasting and matplotlib only for figures. Data format One row per animal per time point, in a CSV: id treatment condition day temp weight score il6 M01 treated endpoint 1 37.15 25.17 0 35.1 M01 treated endpoint 0 37.26 25.25 0 39.5 M01 treated endpoint 1 35.83 23.12 4 162.0 id and a time column ( day , time , hour , …) are required; treatment and condition are optional labels used for grouping and for selecting the reference set. Time may be days, hours, or minutes — just keep it monotonic per animal. The RELSA convention codes the baseline time point as 1 . One row per animal per time point. Average hourly telemetry to one value per interval first (the published models average heart rate, HRV, and temperature, and sum activity). Leave missing measurements empty. They are dropped from the score, never imputed — a missing value treated as "no deviation" biases severity downward. assets/example cohort.csv is a small synthetic cohort (6 mice, 9 days, temperature, body weight, an 0–8 clinical score, and an IL 6 like biomarker) used by every command below, so each one is runnable as written. The four decisions that determine the result Make these explicitly and write them into the methods. Nothing else about the procedure matters as much. 1. Directionality — which variables rise under worsening? Falling is the default (body weight, activity, food intake, burrowing, wheel running). Variables that rise must be declared as turned : clinical scores, inflammatory biomarkers, fever, tachycardia. Get this wrong and the variable contributes nothing at all, silently, because deviations in the "wrong" direction are floored at zero. Body temperature is model dependent — it falls in sepsis and endotoxaemia, rises in fever models. Nothing in the data can settle this for you: in the published sepsis model activity legitimately swings further above baseline than below, so only a variable that never once moves the declared way is detectable, and build reference() warns about exactly that case. 2. The reference set — relative to what? RELSA scores mean nothing without it. Use the group assumed to carry the greatest burden in your model (the published studies use the highest dose or endpoint reaching treatment group). Too mild a reference pushes every score above 1; too severe compresses everything toward 0. Save it with save reference and reuse it with load reference so later cohorts stay on the same scale. 3. Scores with a zero baseline. A clinical score of 0 in a healthy animal cannot be ratio normalized — 0/0 is undefined. Use score scale score=8 to map the score's scale instead (healthy → 100%, worst possible → 200%), which also marks it as turned. This mapping is a modelling choice about how much one score point is worth relative to one percent of body weight; state it. The alternative is to keep the score out of RELSA and use it as an independent endpoint criterion. 4. Which variables are measured throughout. Because the score averages over whichever variables are available, a variable that appears or disappears mid trajectory moves the score by itself. In the published sepsis data, adding body weight — recorded only on the day of euthanasia — drops that animal's endpoint score from 0.93 to 0.83 for no biological reason. relsa scores() warns when composition changes; score the variables present throughout. Workflow Step 1 — compute RELSA scores The reference model is echoed so the scale is auditable: relsa scores.csv holds each variable's weight alongside the score, which is what makes a score explainable — here M01 deteriorating to its endpoint, M03 peaking on day 3 and recovering: A weight of 1.00 means that variable hit the reference maximum; n vars is how many variables entered the score at that time point. Same thing from Python, when you need the objects: Step 2 — forecast the endpoint Train on everything up to the time point before the endpoint, predict the score at the endpoint, and score the prediction: Report all three metrics together. RMSE is point accuracy, PICP the percentage of actual values inside the interval, and MPIW the mean interval width in RELSA units — a model can reach PICP = 100% by making the interval so wide it says nothing, which is exactly what the paper's pancreatic cancer row (PICP 100%, MPIW 7.35, i.e. 735% of the RELSA range) shows. For live monitoring, forecast one step ahead at every time point instead: Two things to know before trusting a forecast: Interpolation is on by default ( interpolate step 0.1 ), because one measurement per day is far too sparse for ARIMA. It buys usable model selection and narrower intervals at the cost of honest uncertainty. Set interpolate step 0 when measurement frequency allows. ARIMA cannot predict a cliff. It assumes stationarity and linearity, so an abrupt collapse in the last hours before an endpoint will not be forecast from a smooth prior trajectory — the paper's own failure case. Act on the upper bound of the interval, and never let a low forecast override an animal that looks unwell. Step 3 — put the score in context with severity zones Thresholds are the minima of the score density — the sparse valleys between clusters of scores. Include endpoint animals, survivors, and shams: the zones are meant to separate those states, so all of them must be represented. Check the bandwidth before believing a threshold. On the published sepsis data this implementation finds minima at 0.355 and 0.655 (published: 0.337 and 0.643) — but a 10% larger bandwidth removes both minima entirely. Run the sweep in references/thresholds and zones.md and report the sweep, not a bare pair of numbers. An empty threshold list is a legitimate answer: the scores form one cluster and there is no data driven place to cut. Boundaries: state these when you report RELSA is an aid to severity assessment, not a decisive parameter. An animal with a low RELSA score that shows other signs of distress must still be handled accordingly. Neither procedure is a validated predictor of death. KDE zones are not regulatory severity gradings. EU Directive 2010/63/EU's categories (non recovery, mild, moderate, severe) are assigned prospectively by a different process. The paper is explicit that its thresholds "should not be confused with regulatory severity gradings" and are not directly translatable to them. Scores are not comparable across reference sets or models. RELSA is relative by construction, and clinical scoring is not harmonized between laboratories. Always report the reference set with the score. The published evidence is a proof of concept : 13 animals across seven models, five of those rows resting on one or two animals. The overall RMSE of 0.069 and PICP of 96% come from 13 endpoint predictions. An underestimated score is the dangerous error , because it discourages attention and can delay a euthanasia decision, whereas an overestimate merely prompts extra care. Reporting checklist A severity analysis is reproducible only if all of this is stated: 1. Outcome measures, their units, and their directionality (which were turned, and why). 2. The baseline time point or window, and which variables were normalized. 3. Any score mapping applied to ordinal variables, with its scale. 4. The reference set : which animals, which group, how many, and why they are assumed to carry the greatest burden. 5. Humane endpoint criteria actually applied in the study, separately from the RELSA score. 6. For forecasts: interpolation step, the selected ARIMA order per animal, and RMSE, PICP, and MPIW. 7. For thresholds: the bandwidth, the number of scores, and a bandwidth sensitivity sweep. 8. Software versions, and the statement that thresholds are model specific and not regulatory gradings. Common pitfalls 1. Wrong directionality — a rising variable not listed in turned contributes exactly zero, silently, and no warning is possible unless it never once falls. Check the reference model table yourself: max reached should be below 100 for a falling variable and above 100 for a turned one, and max delta should be a plausible size for that measure. 2. Normalizing a percentage twice — bwc [%] and mapped scores are already on the percent scale; passing them to normalize flattens them. 3. A zero baseline — a clinical score of 0 makes the ratio undefined; the variable becomes all NaN with a warning. Use score scale . 4. A reference set that does not express the burden — a variable that never deviates in it raises an error rather than dividing by zero, and one that barely deviates inflates every score. 5. Changing variable composition along a trajectory — see decision 4 above. 6. Reading MPIW as a good thing — a wide interval raises PICP while destroying the forecast's usefulness. 7. Reporting a KDE threshold without its bandwidth — thresholds can vanish under a 10% bandwidth change. 8. Treating the forecast as permission to wait — the model cannot see abrupt deterioration, and the humane endpoint criteria of the protocol always take precedence. 9. Comparing RELSA scores between models — only valid within one reference frame. Resources Scripts scripts/relsa score.py — the RELSA procedure: prepare() , build reference() , relsa scores() , relsa weights() , and a ReferenceModel that serialises to J

relsa-severity-assessment