Name Task Data <br>Source Inclusion<br>Criteria Sampling Strategy Waveform-Processing Features Targets Pos vs. Neg Model Link Notes
Hypotension Prediction Index (HPI) Forecasting hypotension defined as MAP<65 MIMIC II + proprietary Edwards database for <br>training and internval validation <br>and prop. Prospective data for ext. V. OR and ICU patients with waveforms - filter out prime examples of clearly hypotensive vs. clearly stable segments of the MAP trajectory and define ref points (see Targets)<br> ART sampled at 100 Hz, preprocessing and heartbeat detection, <br>calculate features for individual beats, each of the features <br>is the median of corresponding features calulcated over all individual <br>heart beats over 20 second time intervals (some features are exceptions)<br>total: 3,022 arterial-waveform features per cardiac cycle, chose the most effective<br>alignment of waveform_window to sample point unclear 3,022 arterial-waveform features per cardiac cycle, chose the most effective<br> positive: a sample point 5, 10 and 15 min after a section min 1 min duration <br>with MAP <65<br>negative: 30 min cont section at least 20 min apart from hypotensive event <br>AND MAP < 75, sample point = center<br>of that section positive: a sample point 5, 10 and 15 min after a section min 1 min duration <br>with MAP <65<br>negative: 30 min cont section at least 20 min apart from hypotensive event <br>AND MAP < 75, sample point = center<br>of that section Logistic Regression https://pubmed.ncbi.nlm.nih.gov/29894315/ Original HPI, no escalation predicted here
Wu et al. 2016 considered 3 tasks: predicting imminent vasopressor need (within next 2 hours),<br>short-term vasopressor need (in the near future but not now), and wean readiness (wean readiness as being able to stop administration completely within 2 hours, and a successful wean as not requiring vasopressors again within 4 hours) Mimic II excluded patients with <12 hours of data or >96 hours of data to avoid a group of fundamentally sicker patients.<br><br>Applying these filters resulted in an initial cohort of 15 695 patients: 4331 were administered vasopressors (positive class) and 11 364 were not (control class). One prediction per patient per hour, from admission until the first vasopressor or the end of stay, with a 4-hour feature lookback. Results are averaged over 5 repetitions.<br><br>The SSAM is trained on positive-class patients only (all hours up to onset for Task 1/2, and the windows around wean for Task 3).<br><br>![[/Projects_4_related_work/Pasted image 20261023094020.png]] no waveforms 19 variables. Six vitals/urine: HR, mean BP, SpO2, temperature, respiratory rate, urine output. Four labs: hematocrit, bicarbonate, potassium, glucose. Seven static: age, sex, first SAPS-I, first SOFA, weight, pacemaker, fall-risk flag. Each variable is z-scored on the training set, rounded to integers from −4 to 4, plus one extra value for “missing”, so 10 discrete values. Three feature sets: raw (previous 4 h of values + static), SSAM (probabilities of each latent state over the last 4 h, i.e. 4k features), and combined. Vasopressor drug and dose are not features Three tasks. (1) Imminent need: a vasopressor within the next 2 h. (2) Short-term need: no vasopressor for the next 4 h but one in the following 2 h, i.e. a gap before onset. (3) Wean readiness: able to stop completely within 2 h, with a “successful wean” meaning no vasopressor again within 4 h. Tasks 1 and 2: the positive class is patients who received a vasopressor, predicted only up to their first administration (multi-vasopressor patients are treated as physiologically different). Controls are patients who never received one. <br><br>The per-hour label rule for negatives isn’t spelled out, could be any hour not followed by onset in the task window. Task 3: positive = the window right before a successful wean, negative = the window right after starting administration (unsuccessful wean). If the numeric data showed no wean, the case was labeled unsuccessful. SSAM: a hidden discrete “physiological state” per hour, with each variable generated autoregressively given the state. The transition function is either a random forest (categorical → categorical) or Gaussian naive Bayes, with separate dynamics per variable. Inference alternates between the hidden state sequences and the transition parameters. Downstream classifiers: linear SVM, naive Bayes, and L2-regularized logistic regression (best overall). https://pmc.ncbi.nlm.nih.gov/articles/PMC6455107/ only the first vasopressor administration is predicted, and drug and dose are ignored. The authors themselves list dose, duration and drug type as future work.
Time-varying Hemodynamic Early Warning Score (TvHEWS) predict "hemodynamic instability" in intensive care unit (ICU) patients retrospective data from the VGHTPE 2010 cohort (Taipei Hospital), validated on MIMIC IV age higher than 20, more than 6h of icu stay, more than one count of vital sign <br><br>Segments within the first 6 h were excluded to focus on subsequent interventions<br><br>-> n = 3671 patients unstable vs. 14.767 stable ![[/Projects_4_related_work/Pasted image 20261022124436.png]]<br><br>One training set per lead time (1–24 h); 2:2:1 train/val/test with 5-fold CV; positives upsampled no waveforms! 46 features (age, vital signs, lab and blood gas measurement, ventilation settings,urine out, organ failue assessment score) defined "hemodynamic instability" in patients by annotating segments where interventions such as the administration of vasoactive/inotropic drugs, fluid therapy, and packed red cells occurred,<br>only first segment that was AFTER 6h intime![[/Projects_4_related_work/Pasted image 20261022124403.png]] Unstable: vasoactive drugs, fluid or PRBC thresholds, features taken hourly before onset. Stable: patients without intervention, observed in a 6 h window after admission 24 individual XGBoost https://link.springer.com/article/10.1186/s13054-025-05553-x - only first intervention segment after 6 h has a label, escalation - so second VP on top or relative increase has no label<br><br>Performance of the 24 models look nearly identical <br><br><br>![[/Projects_4_related_work/Pasted image 20261022125644.png]]<br>-alarm rule (alpha rule) when threhsold of models fire then -> alarm<br><br>
Hemodynamic Stability Index (HSI) predicts the initiation of hemodynamic interventions an hour into the future. eICU, Hospitals were considered reliable if they had charted ≥ 7 infusion drug entries per patient per day, included patients with ≥ 0.75 ventilation and airway records per patient per day in the patient care plan, and ≥ 10 entries per patient per day in respiratory charting tables in eRI database.<br><br>MIMICIII as external validation ![[/Projects_4_related_work/Pasted image 20261022130648.png]]inclusion criteria, with 32,896 patients (prevalence = 18%)<br><br>did not include any samples from the first 6 h of the ICU stay in either the hemodynamically stable or unstable groups during training<br><br> ICU patients were classified into stable and unstable groups. Stable patients did not receive any of the hemodynamic interventions in Table 1. Unstable patients received at least one of the interventions in Table 1 during the ICU stay, including the initiation of pressors or inotropes, administration of a significant dose of fluids in a short time period, or packed red blood cell (PRBC) transfusions. An intervention segment started when any of the intervention criteria was satisfied [10–12]. The intervention segment continued until there was a gap of more than 12 h between consecutive pressors or inotropes, fluid administrations, or PRBC transfusions. no waveforms We selected 33 variables that are routinely acquired in the ICU, including vital signs, laboratory measurements, blood gas measurements, and ventilation settings<br><br>Variables were forward filled up to 2 h for heart rate and systolic blood pressure, and up to 26 h for laboratory measurements and ventilator settings. Invasive and noninvasive blood pressures were combined into a single variable with invasive blood pressure prioritized over noninvasive measurements when both are available Onset of the first intervention segment after 6 h, predicted 1–24 h ahead<br><br><br>![[/Projects_4_related_work/Pasted image 20261022130745.png]] The last set of physiological variables observed 1-h before intervention was used as a positive class sample and a random time 6h after admission from a stable patient was selected as the negative class sample for model training Abstain-Boost model,<br>Each of the 33 classifiers (one for each physiologic variable) outputs a real value, with more positive values indicating a greater risk for hemodynamic interventions. Variable-wise risks are summed and sigmoid transformed for the final probability of hemodynamic intervention. https://link.springer.com/article/10.1186/s13054-021-03808-x it targets initiation of an intervention and uses no medication features,<br><br>has uncertainty build in <br>![[/Projects_4_related_work/Pasted image 20261022131250.png]]![[/Projects_4_related_work/Pasted image 20261022131127.png]]
FIRST-ICU Treatment prediction in the ICU after a 6 hours time-gap<br> - Discrete Status of 7 interventions ( stay on, stay off, wean, onset) for the next 4 hours<br> - temporal 0/1 prediction in the next 6 hours ( one prediction per hour);<br><br> purely associational predictive model MimicIV only patients with singular stays, 18+, minimum of 24hours stays extract hourly sequences of patients vitals, labs, drugs, procedures and meta-data, Handle missing values, <br> <br> Temporal vital and lab variables and static demographics and intervnetions variables all concatenated into a single feature vector for each patient admission at every hourly timestep no waveforms 12 hour observation window (labs,vitals,drugs) -> encoded target 1: Categorical State prediction (onset, wean, stay on, stay off)<br> - prediction window 4 h<br> - target 2: Continuous hourly probability forecasts over a 6 hour horizon<br> - prediction window 6 hour does not apply Graph Neural Network Autoencoder https://www.nature.com/articles/s41746-026-02890-1 Their "cold start" evaluation: By specifically testing cases with no prior intervention history, they're asking: does the model only work because it sees "patient already on norepi" and extrapolates that forward (a trivial, non-causal signal), or does it actually pick up early physiological deterioration signals before any treatment has started?<br>- the Intervention Interaction Attention Module exists because, with a 6-hour horizon and 7 correlated interventions, treating them as 7 independent prediction problems (which is roughly what our per-target XGBoost models do) throws away real structure
Jentzer et al. 2025 predict new-onset shock for at-risk intensive care unit (ICU) patients based on discrete vital sign data from the electronic health record. Mayo Clinic Hospital at Saint Marys yielding 14,753 eligible patients. We excluded the 3322 patients meeting our definition of shock in ICU block one (i.e., lactate >2 mmol/L, receipt of vasopressors, cardiac arrest, or use of temporary MCS) and 126 patients who left the ICU during the first 4 h (deaths or discharges), yielding a final study cohort of 11,305 patients One sample per eligible ICU admission, with the first 4-hour block (hours 0–4) used to calculate input features and the following 4-hour block (hours 4–8) used to assign the outcome label.<br><br>Vital signs were summarized within fixed time blocks; no waveforms! Time-stamped data were extracted from the Mayo Clinic ICU DataMart, including vital signs, laboratory values, clinical assessments, inhospital cardiac arrest (IHCA) and the use of vasoactive drugs and mechanical circulatory support (MCS) The presence of shock was assigned in each ICU block using a simplified definition that included any of the following: lactate >2 mmol/L, receipt of vasopressors, IHCA, or use of MCS. Patients who met this definition of shock during ICU block one (i.e., the first 4 h of the ICU stay) were excluded. Patients were grouped based on the subsequent development of new-onset shock during ICU block two, which was the primary outcome of interest. Positive: patient meets the study's shock definition during hours 4–8: lactate >2 mmol/L, receipt of vasopressors, in-hospital cardiac arrest (IHCA), or use of mechanical circulatory support (MCS). Negative: none of these criteria are met during hours 4–8. Patients meeting the shock definition during hours 0–4 are excluded rather than treated as positive examples. Logistic regression as the baseline; ML models: elastic net penalized regression, random forest, gradient-boosted trees (GBM), and extreme gradient boosting (XGBoost/XGB). https://www.sciencedirect.com/science/article/pii/S0883944125000802?via%3Dihub Snapshot model, assesses first 4 hours per stay once not repeated live moniroting
circEWS Continuous 5-min risk score for "circulatory failure" in the next 8 h, converted to an alarm system with silencing. The goal is early warning of a physiological and treatment-defined state, not prediction of a treatment pure treatment decision HiRID: 55,602 admissions, 7,333 variables, >3 billion observations, with continuous signals every 2 min. External validation: MIMIC-III v1.4 Of 55,602 admissions, 44,506 remain after excluding those before 2008 (PDMS run-in with changing variable IDs), no research consent, and no relevant data. Then excluded: age <16 or >100, mechanical circulatory support, and missing MAP/lactate/HR needed to define circulatory status. 36,098 admissions for development. Variables are reduced from 7,333 → 710 → 209 by merging, e.g. same drug, different dilutions. MIMIC-III: 61,533 → 18,970 with identifiable circulatory status → 9,040 used. All 5-min time points that are not in failure and not ambiguous, about 15 million slices with 3.1% positives. Prevalence is deliberately not rebalanced. About 13M slices train and about 0.8M test. Temporal splits: five overlapping splits of 5 years each (the last year split 1:1 into validation and test, start shifted by 6 months), plus a held-out split of the most recent 10% of admissions (Nov 2015–May 2016) that was not touched during development. 5,278 features from 209 variables, built at every 5-min grid point from imputed data. Six feature types: current value; multi-resolution summaries (4 windows chosen by variable frequency: median, IQR, min, max, trend); instability history (current state, time since last pathologic state, density of pathologic states); measurement intensity (time since last real measurement, share of time points with measurements); shapelets (20 per variable and length, distances over the last 4 h); plus static (age, surgical/emergency admission, APACHE group, height, sex) and time since admission. Drugs are merged into one effective rate or a presence flag (boluses become a rate over their duration of action). Full model: top 500 features / 112 variables. Lite: 176 features / 16 variables. Binary: circulatory failure within the next 8 h, predicted every 5 min while the patient is not in circulatory failure. Each 5-min point is annotated from a 45-min window, where conditions must hold for 30 min (not necessarily consecutive). Circulatory failure = MAP ≤65 mmHg or (not exclusive) vasopressors/inotropes present, and lactate >2 mmol/L. No failure = MAP >65, no vasoactives, lactate ≤2. Ambiguous = no MAP or lactate in the window, or the MAP/drug criterion met with lactate ≤2 (36.5% of time points); these are excluded from training and evaluation. Positive = no failure now, failure within 8 h. Negative = no failure now, none within 8 h. The annotation interpolates lactate using future values, but the features never do. Result: 45,886 events in 11,046 patients, mean duration 320 min. LightGBM gradient-boosted trees, full and compact variants. Compared against a single decision tree on MAP, lactate and vasopressor dose (a threshold-rule baseline), logistic regression, and an LSTM. Raw scores are isotonic-regression calibrated. Alarm system: score above a threshold raises an alarm, then a 30-min silencing period, with the system reset 25 min after the patient recovers. https://www.nature.com/articles/s41591-020-0789-4 No on-therapy escalation, by construction, predictions are only made while the patient is not in circulatory failure, further escalation has no label.
Lilly et al. 2024 (authors are working at CLEW Medical) Two seperate models: HD (hemodynamic): nurse-documented vasopressor initiation ≥6 h after ICU admission, or re-initiation after a 6-h vasopressor-free interval. RF (respiratory failure): physician-documented invasive ventilation initiation, with the same ≥6 h rule UMass Memorial eICU (tele-ICU) program: 7 ICUs in 2 hospitals, 102 adult beds, July 2006–Sept 2017. External: WakeMed (7 ICUs, 2 hospitals, Nov 2019–July 2020, 3,191 stays) and a COVID-19 cohort (513 patients) that did not exist at derivation. Control cohort: 362 recent usual-care UMass patients (June–July 2021) Valid ICU registration, age ≥18, at least 6 h of ICU data. Excluded: heart rate or SpO2 recorded less often than once per 60 min, and any prior ICU admission in the same hospital stay (first ICU stay only). Counts: 59,573 derivation stays (80%), two internal validation cohorts of 6,098 and 6,116 (10% each), 3,191 WakeMed. The model produces a prediction every 15 min, using only data available up to that time, from the start of the stay to discharge. Random 80/10/10 split by stay (the first random draw with roughly balanced sex, unit, LOS and event frequency), not a temporal split. Imbalance is handled with scale_pos_weight and oversampling of positive events. no waveforms age, sex, weight, admission diagnosis, nursing and respiratory flowsheets, vitals, SpO2, labs, and medication orders, mapped with a proprietary ontology, NEWS/MEWS and SOFA subscores. Two separate models. HD (hemodynamic): nurse-documented vasopressor initiation ≥6 h after ICU admission, or re-initiation after a 6-h vasopressor-free interval. RF (respiratory failure): physician-documented invasive ventilation initiation, with the same ≥6 h rule Window-based. Event window = the 8 h before the event timestamp. Non-event windows start at the first time ≥6 h after admission that is not in an event window (or 8 h after a previous non-event window) and end at discharge, the start of an event window, or after 8 h. Windows <4 h are excluded. CatBoost https://pmc.ncbi.nlm.nih.gov/articles/PMC11214904/ The target is vasopressor initiation, including re-initiation after a 6-h free interval, so it does cover some repeat episodes. It does not target dose escalation. I don’t see an exclusion of periods on ongoing vasopressors from the non-event windows, so an alert during continuous therapy would seem to count as a false positive.
Duval et al. 2025 “Will this septic ICU patient start continuous vasopressors 6–48 h after admission?”, using data that ends 2 h before. <br><br>"2–4 h pre-shock warning´" MIMIC-IV v2.2<br>internal validaiton only <br><br>Sepsis is defined with the LCP Sepsis-3 derived table from mimic git Sepsis-3 ICU stays: 31,206<br><br>Positive Cases: first continuous vasopressor 6–48 h after ICU admission, no surgery in the prior 24 h (1,678). Negatives: ICU stay ≥48 h, no surgery from 18 h before to 48 h after admission, no vasopressor at any time (4,803). One sample per patient at one time point, not a rolling prediction over the stay. no waveforms Static: age, sex, weight, height, BMI, Charlson index, SOFA at hour 5. Vitals: HR, mean BP (MBP), RR. Capillary refill time. Labs: albumin, hematocrit, lactate, CRP, total protein. Cardiac output (thermodilution). All measured in the window −6 h to −2 h before initiation. Albumin, total protein and cardiac output were dropped (<1% available). CRT, hematocrit and lactate are only about 35% available. Preferred settings: no forward-filling of sporadic labs, minimum over 4 h for other vitals/labs, hourly values for HR/RR/MBP, random-forest imputation of the rest. No drug or dose features Binary case vs. control classification. A case means a first continuous infusion of norepinephrine, epinephrine, phenylephrine or vasopressin in inputevents. Cases: first continuous vasopressor 6–48 h after ICU admission, no surgery in the prior 24 h (1,678). Controls: ICU stay ≥48 h, no surgery from 18 h before to 48 h after admission, no vasopressor at any time (4,803) Seven algorithms: MLP with weight decay, random forest (best), naive Bayes, radial SVM, FDA, GLMNet, HDDA. Comparators: a re-implemented attention-based bi-LSTM (Kwak et al.; the original code isn’t public, so they adapted Kaji et al.’s code, in Python), MBP alone, and the modified shock index (HR/MBP). Main analysis in R 4.1.3. https://pmc.ncbi.nlm.nih.gov/articles/PMC12706921/ <br>This is a case-control classification, not a monitoring task.<br><br>Only first-ever continuous vasopressor starts in sepsis count, with no drug features.<br>
Bukhari et al. (2025) Multi-label early prediction / clinical deterioration risk assessment. Using the first 24 hours of ICU data, the model predicts which of four care escalation triggers may occur over the following 48 hours, allowing multiple triggers to be predicted simultaneously. MIMIC IV Adult ICU admissions (age ≥18) with recorded charted vital signs and laboratory values. Only the first ICU stay per hospital admission was included. Features were extracted from the first 24 hours after ICU admission. One sample per eligible ICU stay, using the first 24 hours after ICU admission to extract features and hours 24–72 to determine the outcome labels. Only the first ICU stay per hospital admission was included no waveforms demographics: age, gender. Vital signs: mean, minimum, and maximum SpO₂, systolic blood pressure (SBP), mean arterial pressure (MAP), heart rate (HR), and respiratory rate (RR). Laboratory: most recent creatinine. Lactate and pH were considered but excluded due to missingness Four binary labels: (1) respiratory failure, (2) hemodynamic instability, (3) renal compromise, (4) neurological deterioration. Outcomes were assessed during hours 24–72 after ICU admission. Positive: a CET threshold was met during the outcome window. Negative: the corresponding threshold was not met. Respiratory failure: ≥2 SpO₂ readings below 90% or RR >30 bpm. Hemodynamic instability: any MAP 0.3 mg/dL and peak creatinine >1.2 mg/dL. Neurological deterioration: GCS drop >2 points or any sedation-monitoring event. Four classifiers: Logistic Regression, Random Forest, XGBoost, and Neural Networks. https://ieeexplore.ieee.org/document/11343128 This is a case-control classification, not a monitoring task.
Zhang et al. 2026 Predict hemodynamic instability from pre-event vital-sign time series, and investigate the prognostic association of respiratory permutation entropy with instability and mortality. eICU Sepsis diagnosis on ICU admission; first ICU admission within a single hospital admission; age 15–80 years; ICU stay longer than 6 h; no documented cardiac surgery, cardiopulmonary bypass support, or cardiac arrest during hospitalization. For each positive case, a 6-hour pre-event window of respiratory-rate monitoring, recorded every 5 minutes, was selected. Negative-control windows were randomly sampled from eligible patients without instability events no waveforms Time series of respiratory rate, heart rate, and peripheral oxygen saturation (SpO₂)<br><br>age, sex, ethnicity, APACHE IV score, comorbidities, treatments, and ICU length of stay for adjusted analyses. Primary: first hemodynamic instability event. Defined by invasive mean arterial pressure below 65 mmHg within 5 minutes, or initiation of hemodynamic intervention (vasoactive/positive inotropic drugs or fluid resuscitation). Secondary: ICU mortality and hospital mortality. Positive: 6-hour respiratory-rate monitoring record preceding the first hemodynamic instability event, with the event occurring at least 6 h after ICU admission and at least 6 h of consecutive prior respiratory monitoring. Negative: randomly extracted 6-hour monitoring record from a patient who never experienced an instability event and did not die during XGBoost, LightGBM, and deep neural network (DNN) models; https://pmc.ncbi.nlm.nih.gov/articles/PMC12849095/ Another "hemodynamic instability" prediction proposal
ChronoSynthNet Jointly forecasts norepinephrine infusion requirements and detects impending hypotension in ICU patients with septic shock. MIMIC-IV v2.1 Age ≥18; Sepsis-3 sepsis; ICU stay ≥6 h; more than one vital-sign observation; received norepinephrine; initial norepinephrine administration ≥6 h after ICU admission. Predictors were temporally aligned to the initial norepinephrine administration, which occurred ≥6 hours after ICU admission. Hourly time-varying data before this reference point were extracted. The most recent vital signs and ventilator settings within a 2-hour window were used; the most recent laboratory values within 24 hours were used. Measurements outside these windows were treated as missing. The time series was divided into overlapping windows of size 2 with 50% overlap. no waveforms 15 variables retained from 24 initially selected candidates. Included vital signs, laboratory tests, and ventilation-related variables. Most important features included lactate, platelet count, non-invasive systolic and diastolic blood pressure, creatinine, and PaCO₂. Other features included heart rate, ALT, potassium, PaO₂, respiratory rate, chloride, ionized calcium, SpO₂, and tracheostomy. Values outside prespecified physiological ranges were treated as missing; missingness was handled using masks. Invasive blood pressure was used when non-invasive measurements were unavailable. Two prediction targets: (1) norepinephrine infusion rate, a continuous regression target; and (2) upcoming hypotension, a binary classification target, defined using MAP positive in the hypotension-target defined by MAP threshold Transformer encoder using multi-head attention, LSTM layers, and a dynamic feature-weighting network. https://pmc.ncbi.nlm.nih.gov/articles/PMC12432635/ This approach does forecast norepinephrine dose in patients already on norepinephrine but as a regression plus hypotension detection (from two heads), not as an escalation event with persistence semantics
Mendoza et. al. 2025 Early prediction of future Mechanical Circulatory Support implantation need in pediatric ICU patients with acute decompensated heart failure Single-center retrospective cohort from the pediatric cardiac intensive care unit (CICU) at Texas Children’s Hospital, Houston, covering 2015–2018.<br><br>106 patients retrospective monitor data available; invasive arterial blood pressure waveform available; treated with inotropic agents such as milrinone or epinephrine. Excluded patients supported with ECMO or other MCS therapy during the first two hospital days. For each patient, signals were analyzed over 48 hours following initiation of the first inotropic agent, or from CICU admission if the patient was already receiving an inotrope. Low-frequency signals were divided into non-overlapping 5-minute windows; high-frequency ECG and ABP waveforms were divided into 10-second segments. The resulting signal features represent the two-day observation period, and predictions are made at the patient level.<br>-> No sliding window raw ECG and ABP waveforms converted into latent features using a variational autoencoder (VAE). age, sex, pre-existing heart failure, previous admission, body surface area, left ventricular ejection fraction, shock at admission, heart rate, previous ventricular tachycardia, BNP, BUN, labs, Low-frequency signals: heart rate/BPM, PVC alarm, SpO₂, ST-segment deviations across ECG leads, and pulse pressure (SBP − DBP) calculated over 5-minute windows (mean,max etc.) Binary classification of whether the patient will require MCS implantation following the observation period. Positive: patient required MCS support (n = 50). Negative: patient did not require MCS support (n = 56).