Validation and clinical applicability of the Hypotension Prediction Index in a general ICU population: a prospective observational cohort study Study acronym Prediction of Hemodynamic Instability in Patients Admitted to the ICU; the PHYSIC study

Validation and clinical applicability of the Hypotension Prediction Index in a general ICU population: a prospective observational cohort study Study acronym Prediction of Hemodynamic Instability in Patients Admitted to the ICU; the PHYSIC study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validation and clinical applicability of the Hypotension Prediction Index in a general ICU population: a prospective observational cohort study Study acronym Prediction of Hemodynamic Instability in Patients Admitted to the ICU; the PHYSIC study Lotte E. Terwindt, Denise P. Veelo, Max Ligtenberg, Jaap Schuurmans, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4169157/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Hypotension is associated with adverse outcomes in patients admitted to the intensive care unit (ICU). The application of an arterial blood pressure derived algorithm predicting hypotension significantly reduced hypotension during surgery. This Hypotension Prediction Index (HPI), calculates the likelihood (range 0-100) of hypotension occurring within minutes. In this study, the performance and clinical applicability of HPI is assessed in ICU patients. Objectives: The primary objective was to assess overall performance of the HPI in ICU patients. Secondary objectives were to assess; the time to hypotensive events, change in the average sensitivity of HPI-85 preceding a hypotensive event, performance of HPI at clinical relevant threshold (HPI ≥ 85), and differences in patient subgroups. Methods: We performed a prospective cohort study in an adult general ICU population of a tertiary academic medical centre using continuous arterial pressure waveform data. Hypotension was defined as mean arterial pressure below 65 mmHg for at least one minute. The predictive ability of HPI was evaluated using a forward analysis, calculating sensitivity, specificity, positive predictive value (PPV), time to event, receiver operating characteristic (ROC) curve and precision recall (PR) curve. Results: In 499 included and analysed patients, HPI showed an excellent predictive performance (area under ROC curve 0.97, PR curve 0.95), with a statistical optimum calculated at HPI 95 (Youden Index 0.87). Employing HPI ≥ 85 as an alarm resulted in a sensitivity of 99.7%, specificity of 76.3%, PPV of 83% and a median time to hypotensive event of 160 sec [IQR 60–380]. There was no difference in HPI performance between different patient subgroups. Conclusions: HPI showed excellent performance in the prediction of hypotension in a general ICU population, without differences between subgroups. However, the average time between alarm (HPI ≥ 85) and the onset of hypotension is relatively short, which might affect the applicability and added value in an ICU setting. Trial registration This study was registered with the Netherlands Trial Register (NTR7349). The study was submitted and accepted for registration 2018-07-04, before the first patient was included. ( https://www.trialregister.nl/trial/7150 ). Source ID: W18_142#18.176 Area under the threshold arterial waveform blood pressure hemodynamic monitoring machine learning artificial intelligence Figures Figure 1 Figure 2 Figure 3 Figure 4 Key Points This is the first validation of HPI in a broad subset of ICU patients using the forward tumbling method including all data points. HPI is highly accurate in predicting hypotension in ICU patients, and allows for the initiation of a preventive rather than reactive treatment of hypotension up to three minutes before its actual onset. The HPI model showed excellent performance regardless of diagnoses at ICU admission. The average time between alarm (HPI≥85) and the onset of hypotension is relatively short, which might affect the applicability and added value in an ICU setting. A lower HPI threshold (75-85) would facilitate a longer time to event, and might prove to be more applicable in daily ICU practice . Introduction Hypotensive events during admission on the intensive care unit (ICU) occur frequently and treatment is challenging.[ 1 ] Both the depth and duration of hypotension are associated with adverse outcomes including mortality in ICU patients. [ 2 – 6 ] Pro-active hypotension treatment with the use of a predictive alarm could potentially reduce the number and severity of hypotensive events, by providing the opportunity to intervene before hypotension occurs and could potentially reduce the number and severity of hypotensive events, by providing the opportunity to intervene before hypotension occurs. [ 7 – 10 ] The Hypotension Prediction Index (HPI) is a machine-learning derived algorithm that alarms for impending hypotension, with values ranging from 0 to 100. [ 11 ] The algorithm was trained on arterial blood pressure (BP) waveform data of 1334 surgical and ICU patients to calculate the probability of impending hypotension. [ 11 ] Several studies, using different analysis methods, showed a hypotension sensitivity and specificity prediction of 80–90%, with the time of alarm onset to hypotension, predicted by HPI, ranging from 4–15 minutes. [ 11 – 15 , 31 ] The clinical performance of HPI has been demonstrated in multiple samples of surgical patients.[ 7 – 10 , 14 – 16 ] However, data on predictive and clinical performance of HPI in ICU patients is lacking, as only one external validation study has been performed in a small group of ICU patients with COVID-19. [ 13 ] Assessment of the performance of HPI in a diverse ICU patient population is warranted to assess its utility in this setting. Additionally, logistics in the operating room differs from the ICU as nurses and clinicians are not continuously at bedside, potentially delaying treatment initiation. Therefore, it is important to assess whether the average time between prediction and actual onset of hypotension allows for implementation of HPI in the ICU setting. In this prospective observational study, we assessed the applicability of HPI in the ICU. The primary objective was to assess the overall performance of HPI to predict hypotension in general ICU patients using the forward tumbling method. Hypotension was defined as a mean arterial pressure (MAP) below 65 mmHg for at least one minute. [ 3 , 5 , 17 , 18 ] Secondary objectives included estimation of the time to hypotensive events at different HPI alarm thresholds, performance metrics at a clinical relevant HPI threshold of 85, change in the average sensitivity of HPI-85 in the minutes preceding a hypotensive event, differences in performance at various prediction time windows and the discriminative performance of HPI in different subgroups based on patient category. Materials and Methods Study design This was a single center, prospective, observational study in patients admitted to the mixed surgical and non-surgical ICU of a tertiary academic medical centre in Amsterdam, the Netherlands. This study was approved by the Medical Ethical Committee of Amsterdam UMC location AMC, the Netherlands in May 2018 (Source ID: W18_142#18.176) and registered with the Netherlands Trial Register (NTR7349). Data were collected from the 9th of September 2018 until the 30th of May 2019. The study was conducted in compliance with the Declaration of Helsinki (Fortaleza, 2013), the Dutch Medical Research Involving Human Subjects Act and Good Clinical Practice. Participants Patients ≥ 18 years, newly admitted to the ICU, either during the day or evening/night period and who received an arterial line as part of standard care, were eligible for inclusion. Exclusion criteria were: an inability to measure continuous BP data with an arterial line, expected admission time < 8 hours, controlled hypotension or hypertension with a target MAP lower or higher than 65 mmHg, severe arrhythmias and logistic difficulties (e.g. transfer to another hospital). To obtain sufficient data for validation, we intended to collect data for an expected minimum of eight consecutive hours. Trained and delegated members of the study team screened patients. Written informed or deferred consent was obtained from all participants. Measurements and signal quality Continuous BP data were collected with a hemodynamic monitor (the EV1000, Edwards Lifesciences LTD, Irvine, CA, USA) using the arterial line system of patients with Flotrac sensors. The monitor was blinded for the treatment team and was solely used for study data collection. Hemodynamic variables and BP data were derived directly from arterial waveform analyses to the EV1000 monitor. Intra-arterial BP was measured with a five French cannula in the radial artery or if placement in the radial artery was not possible, the brachial or femoral artery. Before starting measurements, the transducer of the arterial line was placed at the level of the right atrium and zeroed. We frequently performed fast flush tests to ensure the system was neither over- nor underdamped.[ 19 ] Hemodynamic data were stored on an internal disk. Anonymized data files were copied to a USB flash drive and stored on a secure hospital server; only accessible to members of the study team. Patient characteristics (age, weight, height, sex, intoxications, medical history, (home) medication, ventilator settings, reason for ICU admission and sequential organ failure assessment score (SOFA)) were all extracted from the electronic patient database (2018, Epic Systems Corporation, Verona, Wisconsin, USA). All de-identified data were entered into a good clinical practice compliant database (Castor EDC™, version 2019.1.5) and handled according to the General Data Protection Regulation of May 2018. A 10% check was performed after data entry in Castor EDC™ and less than three percent errors were found which was considered satisfactory. Definitions and analyses The dataset was reviewed for accuracy, missing values, and outliers. Distribution of data was assessed using histograms, QQ-plots and boxplots. Categorical data were described as frequencies (percentages) and group differences were assessed with a Pearson’s Chi-squared (if frequencies were ≤ 5) or a Fisher’s exact test. Continuous variables were presented as mean with standard deviation (SD) or median with first and third quartile [Q1-Q3] when appropriate. Differences in continuous data were calculated using t-tests, Kruskal Wallis or Mann-Whitney-U depending on distribution. Statistical significance was assumed at p < 0.05. A hypotensive event was defined as an absolute MAP value below 65 mmHg for at least one minute. For each patient, monitoring time, number of hypotensive events, time spent in hypotension, area under the threshold and the time weighted average (TWA) of hypotension were determined. The combination of depth and duration of hypotension was expressed in TWA. The TWA was calculated by dividing the cumulative sum of the area under the threshold (AUT) of hypotension by the total duration of the measurement period. The units for AUT are mmHg*min and the units for TWA are mmHg. [ 20 ] Primary objective The primary objective was to assess the performance of HPI in predicting hypotension in a general ICU population. The predictive ability of HPI was evaluated at each threshold (0-100) using a forward tumbling analysis similar to Wijnberge et al. and van der Ven et al. [ 12 , 13 ] A grey zone (MAP 65–75 zone excluded for analysis) has not been used in this validation method. The validation was divided into three stages: 1) data preprocessing, 2) labeling of data according to definitions of (no-) alarms and (non-) hypotension and 3) calculation of performance metrics (Appendix A). Data were labelled with a tumbling window approach. After exceeding the studied HPI threshold, the next interval of 20 minutes was screened for the onset of a hypotensive event. The time window was then ’tumbled’ or ’flipped’ ahead in time, so windows did not overlap. Data were sequentially labelled from the start to the end of the data timeline of a patient. As long as no alarm was encountered, each past 20 minute window was labelled based on the occurrence of hypotension in that window. Upon an alarm, a new 20 minute window starting from the alarm was forced. Every next window only started once hypotension had resolved. A subsequent hypotensive episode, as well as an HPI alarm only counts as two separate events when respectively the MAP or the HPI normalized for at least one minute. Timeframes were labelled as true positive (TP), false positive (FP), true negative (TN), or false negative (FN). (Supplemental table 1 ., Appendix B) To correct for overrepresentation of TNs, a maximum of one non-hypotensive event (MAP > 65 mmHg) was maintained per 20-minute time interval after a HPI alarm. Interventions were automatically assessed by excluding measurements that were most likely influenced by clinical interventions, such as administration of vasopressors or application of positional changes resulting in a rapid rise in BP. [ 13 ] The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for HPI were calculated, ranging from 0 to 100 in steps of five. Furthermore, Youden’s J statistic was calculated to determine the statistically optimal HPI threshold. [ 21 ] Discriminative performance was further evaluated using the Receiver Operating Characteristic (ROC) curve and precision recall (PR) curve. [ 22 – 24 ] (Appendix C) For both the area under the ROC and PR curve, values between 0.7 to 0.8 were considered acceptable, while values above 0.8 were considered excellent.[ 25 ] Secondary objectives Secondary objectives included estimation of the time to hypotensive events at different HPI alarm thresholds, performance metrics at a clinical relevant HPI threshold of 85, change in the average sensitivity of HPI-85 in the 1 to 20 minute(s) preceding a hypotensive event (in steps of one minute), differences in performance at various prediction time windows and the discriminative performance of HPI in different subgroups based on patient category. Median time-to-event and event rate were determined for every HPI threshold. The event rate is computed as the number of hypotensive events that follow an HPI alarm divided by total alarms. Performance at HPI 85 Earlier studies employed 20 minute prediction windows, and showed that most hypotensive events followed HPI alarms within several minutes. [ 13 ] [ 15 ] Hence, 20 minute timeframes might include late onset hypotensive events not necessarily associated with concurrent HPI values. To this extent, to assess the effect of shrinking the prediction window (to 5, 10, and 15 minutes) on the sensitivity and specificity of HPI-85 was attempted. In sensitivity analyses, HPI performance was evaluated in clinically relevant subgroups of patients that were stratified based on the diagnoses at time of their ICU admission. Subgroups analysed were: patients with cardiogenic shock, distributive shock, admission post cardiothoracic surgery and admission due to a subarachnoid hemorrhage. Data analyses were performed with IBM SPSS® software (version 25.0) and MATLAB (The MathWorks, Inc., Natick, MA, USA) version 9.5.0 (R2018b). Results Patient characteristics A total of 514 patients meeting the inclusion criteria were screened and 499 patients were included in the data analyses. (Fig. 1 ) Table 1 summarizes the patients’ characteristics. The mean age was 61 years (14), most patients were male (66%), the median SOFA score was 10 [ 8 – 12 ]) and the most common reason for ICU admission was post-surgery (43%). Most patients required mechanical ventilation (72%) and received vasoactive medication (61%). The majority of measurements (61%) were obtained during daytime. The median monitoring time per patient was 7 hours and 21 minutes (441 minutes [411– 962]). The sample contained 759 hypotensive events, with a median of six [ 2 – 13 ] events per patient. A patient showed an average of one event per 73 minutes for a median duration of 52 minutes [5-170] per event, resulting in a median TWA of hypotension of 0.3 mmHg [0.03-1.0] Table 1 Baseline patient and monitoring characteristics Baseline parameters Total number of patients 499 Sex, male, n (%) 327 (66) Age, years, mean (sd) 61 (14) Number of patients older than 65 years, n (%) 221 (44) Weight (kg), mean (sd) 82.97 (19.5) Height (cm), mean (sd) 174 (9.9) BMI, mean (sd) 27 (6) SOFA score, median [Q1-Q3] 10 (8–12) Vasoactive medication during measurements, n (%) 302 (61) Mechanical ventilation, n (%) 358 (72) Measurement details Monitoring time per patient (minutes), median [Q1­Q3] 441 [411–962] Number of daytime measurements, n (%) 305 (61) Number of night­time measurements, n (%) 194 (39) Reason of ICU admission Respiratory failure, n (%) 57 (11) Neurological disease, n (%) 82 (16) Subarachnoid haemorrhage, n (%) 51 (10) Sepsis, n (%) 38 (8) Cardiac shock/other cardiac, n (%) 19 (4) Postoperative after surgery, n (%) 216 (43) Cardiothoracic surgery, n (%) 199 (40) Assigned shock groups Cardiogenic shock, n (%) 66 (13) Distributive shock, n (%) 94 (19) Hypovolemic shock, n (%) 12 (2) Obstructive shock, n (%) 2 (0.4) Combination type of shock, n (%) 32 (6) Nonshock classification, n (%) 293 (59) Haemodynamic data of patients with hypotension MAP < 65 mmHg) TWA per patient (mmHg), median [Q1-Q3] 0.3 [0.03-1.0] AUT MAP 65 mmHg per patient mmHg.min, median [Q1-Q3] Number of events per patients, median [Q1-Q3] 6 [ 2 – 13 ] Total duration of events per patient (min), median [Q1-Q3] 52 [5-170] Total percentage duration of measurement in hypotension (%), median [Q1-Q3] 9.3 [0.7–29.1] Statistic presented as mean (standard deviation), median [first quartile, third quartile], or number of patients (%). Abbreviations: MAP, mean arterial pressure; BMI, body mass index; SOFA, sequential organ failure assessment; TWA, time weighted average. Primary objective; validation and performance of HPI in the ICU Figure 2 shows the discriminatory ability of HPI, with an area under the PR curve of 0.95 and the area under the ROC curve of 0.97. The optimal statistical threshold for the forward tumbling validation method was found at HPI 95 for all statistical optimums: Youden Index (0.87), and minimal difference between sensitivity (0.97) and specificity (0.89). Table 2 displays sensitivity, specificity, Youden’s J statistic, PPV, NPV, time to event, and event rate for HPI thresholds between 0 and 100 at incremental intervals of five. Table 2 a HPI, overview thresholds* HPI threshold Sensitivity Specificity Youden’s J statistic PPV NPV Event rate HPI with hypotensive event Total HPI alarms 0 1 0.0027 0.0027 0.6673 1 0.29 [0 -0.64] 11146 23098 5 1 0.11 0.11 0.6713 1 0.30 [0 -0.64] 11133 22269 10 1 0.1676 0.1676 0.6757 1 0.33 [0 -0.66] 11151 21778 15 1 0.23 0.23 0.683 1 0.36 [0 -0.66] 11150 21247 20 1 0.2821 0.2821 0.6913 1 0.36 [0 -0.66] 11151 20744 25 1 0.3323 0.3323 0.6989 1 0.37 [0 -0.68] 11147 20219 30 1 0.3857 0.3857 0.7082 1 0.39 [0.03 -0.68] 11139 19632 35 1 0.4494 0.4494 0.7223 1 0.43 [0.06 -0.71] 11151 18907 40 1 0.5069 0.5069 0.7381 1 0.47 [0.11 -0.73] 11179 18220 45 1 0.54 0.54 0.748 1 0.50 [0.13 -0.73] 11222 17806 50 1 0.5721 0.5721 0.7582 1 0.52 [0.17 -0.75] 11214 17408 55 0.9999 0.5991 0.599 0.7667 0.9997 0.53 [0.19 -0.75] 11217 17051 60 0.9999 0.6271 0.627 0.7772 0.9997 0.57 [0.20 -0.76] 11205 16635 65 0.9999 0.6584 0.6583 0.7878 0.9998 0.59 [0.24 -0.78] 11174 16172 70 0.9994 0.688 0.6874 0.7998 0.9989 0.61 [0.29 -0.80] 11146 15754 75 0.9991 0.7136 0.7127 0.8104 0.9985 0.65 [0.33 -0.82] 11111 15379 80 0.9981 0.7341 0.7322 0.8172 0.9969 0.67 [0.38 -0.82] 11000 14930 85 0.9972 0.7625 0.7597 0.8304 0.9957 0.70 [0.41 -0.84] 10925 14461 90 0.9935 0.8054 0.7989 0.8508 0.9911 0.74 [0.50 -0.88] 10690 13657 95 0.9764 0.8963 0.8727 0.8979 0.976 0.85 [0.67 -0.94] 9743 11397 100 0 1 0 - 0.6079 - - 0 0 Abbreviations: Se , sensitivity; Sp , Specificity; PPV , positive predictive value; NPV , negative predictive value. F1 (score: 0.94), Youden, Min. diff. Se en Sp, Event rate (detections / alarms = True positives / (True positives + False positives) HPI with hypotensive event; all detections of HPI followed by an hypotensive event. Total HPI alarms: An alarm is defined by a combination of an HPI value and the alarm threshold. Data presented as median [Q1-Q3] *20 minutes interval scanrange Table 2 b : Time-to-event Analysis, HPI thresholds HPI threshold Median Time to Event (sec) 0 200 [80–440] 5 200 [80–440] 10 200 [80–440] 15 200 [80–440] 20 180 [80–440] 25 180 [80–440] 30 180 [80–440] 35 180 [80–420] 40 180 [80–420] 45 180 [80–420] 50 180 [80–420] 55 180 [80–420] 60 180 [80–420] 65 180 [60–420] 70 180 [60–400] 75 180 [60–400] 80 160 [60–400] 85 160 [60–380] 90 160 [60–360] 95 140 [60–320] 100 - - * True positive samples are used for estimating time-to-event. HPI range for all TPs at a given threshold is provided for clarity Secondary objective; Time to event and performance at HPI 85 An increase in HPI threshold from 65 to 95 resulted in a decrease of average time between the alarm and onset of hypotension. When employing an HPI threshold of 85, the median time to hypotension was 2.7 [1 to 6.3] minutes. No alarm preceded in 35 of the 759 (4.6%) hypotensive events. Sensitivity increased from 75–80% in the time window 30 to 10 minutes before the onset of hypotension, thereafter, sensitivity increased more rapidly. (Fig. 3 a and 3 b) As described in Table 2 , the increase in HPI value itself was proportional to the decrease in time to event and increase in hypotension occurrence (event rate). This trade-off between HPI threshold, positive predictive value and time-to-event is visualized in Fig. 4 . Secondary objective; changing the prediction window duration Table 3 represents the performance metrics of different prediction window durations for a HPI threshold of 85. For prediction window durations of 5 and 20 minutes, the area under the PR curve was 0.69 and 0.95, respectively. An increase in the prediction window resulted in an increase in PPV, but a decrease of specificity (Supplemental Fig. 1). Table 3 Performance metrics for different prediction windows, supplemented with a leading neutral buffer to a total of 20 minutes, for an HPI threshold of 85. Prediction window Sensitivity 5 min 10 min 15 min 20 min 0.989 0.9944 0.9960 0.9972 Specificity 0.4112 0.5953 0.6987 0.7625 PPV 0.3357 0.6167 0.7528 0.8304 NPV 0.9922 0.9939 0.9947 0.9957 AUROC 0.859 0.938 0.961 0.973 AUCPR 0.695 0.876 0.931 0.951 Abbreviations: PPV, positive predictive value; NPV, negative predictive value; AUROC, area under the ROC curve; AUCPR, area under the Precision Recall curve. Leading neutral buffer: Every HPI value is regarded to cast a prediction over the succeeding 15 minutes, as the HPI model was developed with waveforms up until 15 minutes prior to onset of hypotension. Therefore, a sliding window with a duration of 15 minutes was used to assess HPI performance. The correctness of every alarm is based on the occurrence of hypotension in this window. A leading neutral buffer of 5 minutes was used, so that alarms with a time to hypotension of 15 to 20 minutes were not labelled as a FP. Secondary objective; HPI performance in patient category subgroups Subgroup performance with an alarm threshold of 85, including PR and ROC curves are presented for each subgroup analysis. (Table 4 , Supplemental Fig. 2) In line with the overall population, HPI showed excellent performance in each subgroup of ICU patients. Only in patients with a subarachnoid hemorrhage a lower, but still an excellent area under the PR curve was found (0.85). Table 4. Performance per subgroup, with an HPI alarm threshold of 85. Subgroup CAPU Admission SAH admission Cardiogenic shock Distributive shock Sensitivity Yes No Yes No Yes No Yes No 0.99 0.99 1 0.99 0.99 0.99 0.99 0.99 Specificity 0.69 0.71 0.97 0.73 0.55 0.79 0.64 0.78 PPV 0.86 0.86 0.81 0.83 0.82 0.83 0.80 0.83 NPV 0.99 0.99 1 0.99 0.99 0.99 0.99 0.99 AUROC 0.967 0.974 0.988 0.971 0.948 0.976 0.953 0.976 AUCPR 0.957 0.928 0.846 0.953 0.962 0.950 0.940 0.953 Abbreviations: PPV , positive predictive value; NPV , negative predictive value; AUROC , area under the ROC curve; AUCPR , area under the Precision Recall curve; CAPU , cardiothoracic surgery; SAH , subarachnoid haemorrhage. Discussion This first study of the performance of HPI in a general ICU population showed excellent performance in predicting hypotension. Moreover, the discriminative ability of the HPI algorithm in this cohort can be considered adequate. Time to hypotensive events increased by decreasing the HPI alarming threshold, and a decrease in the prediction window resulted in an increase in PPV, but a decrease in specificity. The HPI model showed excellent performance in all subgroups of patients. While previous studies already showed excellent performance of HPI to predict hypotension in a smaller group of ICU COVID-19 and perioperative patients, [ 7 – 10 , 12 – 14 , 16 , 31 ] insight in the performance and applicability of HPI in the general ICU population is currently lacking. Therefore, we performed a study in a large group of ICU patients with different diagnoses and a wide variety in possible underlying causes of hypotension. In this sample of a general ICU population, we aimed to assess topics that were deemed relevant to be assessed before considering the application of HPI in such a high complex setting. Thereto, we assessed the overall accuracy of HPI, the probability of hypotension onset at different HPI alarm thresholds and the time between the onset of an alarm and the actual occurrence of hypotension. We furthermore evaluated whether the performance of HPI differed in various patient subgroups. Our findings indicate that HPI is highly accurate in predicting hypotension in the general ICU population. As expected, accuracy of HPI increased both when alarms occurred closer to the onset of hypotension, and when higher alarm thresholds were employed. For adequate clinical applicability, an optimum between correct prediction of hypotensive events and the prevention of unnecessary treatments is essential. This optimum is normally found by assessing the balance between sensitivity and specificity of a prediction model. For all HPI thresholds, sensitivity was > 0.9, while specificity steadily increased with HPI thresholds, with an optimal statistical sensitivity and specificity trade-off calculated at a threshold of HPI 95. However, in a clinical environment, clinicians ultimately desire time-series based predictive alarms to not only provide accurate, but also timely notifications of upcoming events. This emphasizes the necessity to additionally assess the trade-off between the positive predictive value and time-to-event at different HPI alarming thresholds. In previous research [ 7 – 9 , 15 , 16 ], an alarm threshold of 85 was used to initiate treatment in a perioperative setting. However, in contrast to this setting, the time between onset of hypotension and initiation of direct treatment is likely to be longer in an ICU setting, due to logistical challenges, as physicians and nurses oversee more than one patient. In this context, the average treatment window of three minutes found in this study would still facilitate a more timely treatment, but might prove too small to also allow for the actual prevention of hypotensive events. If, for example, a PPV of 80% and a specificity of 70% would be considered as the lower bound to initiate preventative treatment, employing a HPI alarm of 75 would result in additional response time, without neglecting these boundaries. Ultimately, these boundaries and the corresponding alarm value should be at the discretion of the treating clinician. Moreover, these boundaries might even have to differ between patients to provide individualized hemodynamic care.[ 26 ] To fully understand its impact on outcomes and logistical aspects, follow-up studies are essential. Our statistically optimal HPI threshold of 95 differs in comparison to previous findings, which might be explained by the inclusion of a larger cohort of general ICU patients and due to the utilization of the forward tumbling validation method in this study, similar to the approach by Wijnberge et al. and van der Ven et al.[ 10 , 12 , 13 , 27 ] Selection bias and the specific validation methods in earlier studies could potentially explain a relevant proportion of the differences in predictive ability. [ 28 ] When interpreting earlier studies, it is important to realize a number of confounding factors. In general, interpreting results of HPI validations (internal and external) comes with several drawbacks because of dependency on type of patients included, the MAP threshold labelled as hypotension, the role of the underlying cause of hypotension (preload, afterload, contractility), the role of clinical manipulation or decision making and type of validation method (forward or backward in combination with sliding or tumbling). With the forward tumbling method as described in this study, each hypotensive event is unique. In addition, this validation method seems most appropriate with regards to the intended clinical use of HPI, only the onsets of alarms were labelled, indicating the intended initiation of proactive treatment. Each individual alarm was assessed using non-overlapping time windows, in contrast to the forward sliding method where each individual prediction is annotated first with an increased risk of overlap between HPI and hypotensive events, thereby avoiding overestimation of results (sensitivity/specificity). This feature strengthens the generalizability of the results to the clinical (ICU) setting. However, important to note is that an element was added to the definition of an alarm, which may have clinical consequences. In this forward tumbling protocol, a minimal duration of one minute of HPI values above alarm threshold was added to the alarm definition. This constraint raises alarm criteria and reasonably reduces false positives. This would only be justified as long as the clinical protocol of HPI use would also incorporate this minimum duration. The downside of this constraint is the reduction of timeliness of the eventual alarm. In addition, this protocol does not consider that user behaviour may change when HPI decreases to subthreshold levels after the initial alarm, which could make the clinician cancel the proactive treatment. Ultimately, the responsibility for the appropriate clinical use of HPI remains with the clinician and the impact of its effectiveness is highly dependent on the behaviour of end-users. The HPI model showed excellent performance in all subgroups of patients. For patients with a distributive shock, cardiogenic shock and patients admitted after cardiothoracic surgery, the HPI model showed the best trade­off between sensitivity and PPV. The HPI showed a slightly smaller, but still excellent performance in patients admitted due to a subarachnoid hemorrhage. This difference might in part be explained by the lower prevalence of hypotension in this population, combined with the underlying pathophysiology. This patient category commonly has an elevated BP due to increased intracranial pressure, although patients with controlled hypertension with a target MAP higher than 65 mmHg were excluded. Limitations and strengths This study focused on patients with a target MAP of 65 mmHg. Validation of the algorithm for higher or lower MAP values was not done in this study, but will be clinically relevant if this algorithm is to be applied in clinical practice in the future. Registration of clinical interventions or treatments by personnel around events were missing due the observational character of this study. This could affect the prediction and rate of occurrence of an event after an alarm has been triggered. However, BP data during interventions were therefore not included for analysis. Duration of measurements were eight hours, while median length of stay in the ICU was much longer in most patients. In this study, we included most patients immediately upon their admission to the ICU and there may have been more hemodynamic instability during these measurements compared to measurements that recorded the entire admission period. In this study, we primarily treated each minute of HPI surpassing the threshold as an alarm. Contrarily, when inspecting specific HPI values in a certain range: e.g. 45 < HPI < 50 (Supplemental table 3 ), a decrease in time to event is observed with increasing HPI. The similarity in time to event (Table 2 ) is most likely explained by HPI rapidly increasing when hypotension is imminent, as the thresholding approach does not take into account HPI values as long as they surpass the threshold. Even though, this 'binning' of HPI values is clinically less intuitive compared to the thresholding approach we added both for clarity and transparency. Conclusions In conclusion, HPI showed excellent performance in the prediction of hypotension in a general ICU population, without differences between subgroups. However, the average time between alarm (HPI ≥ 85) and the onset of hypotension is relatively short, which might affect its applicability and added value in an ICU setting. A lower HPI threshold (75–85) would facilitate a longer time to event, and might prove to be more applicable in daily ICU practice. Intervention studies are necessary to evaluate implementation barriers and assess the effect on relevant patient outcomes. Abbreviations AUT Area under the threshold BP Blood pressure FN False negative FP False positive HPI Hypotension prediction index ICU Intensive care unit MAP Mean arterial pressure NPV Negative predictive value PPV Positive predictive value PR Precision recall ROC Receiver operator characteristic SD Standard deviation SOFA Sequential organ failure assessment TN True negative TP True positive TWA Time weighted average Declarations Ethical approval and consent to participate This study was approved by the Medical Ethical Committee of Amsterdam UMC location AMC, the Netherlands in May 2018 (Source ID: W18_142#18.176). This study was registered with the Netherlands Trial Register (NTR7349). The study was conducted in compliance with the Declaration of Helsinki (Fortaleza, 2013), the Dutch Medical Research Involving Human Subjects Act (WMO) and Good Clinical Practice (GCP). Consent for publication Not applicable. Availability of data and materials APV and LT had full access to all the data in the study and takes responsibility for the integrity and accuracy of analyses of the data. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The Department of Anesthesiology of the Academic Medical Center received financial support for this project from Edwards Lifesciences. The investigators (APV, DPV) have potential conflicts of interest involving the work under review since they received consultancy fees from Edwards Lifesciences. None of the investigators of the AMC have any form of (in) direct ownership in the software or hardware of Edwards and/or subject of this study. Also no rights or claims to rights exist that might lead to financial gains for any of the authors or the AMC as an institution. Funding After design of the trial, Edwards Lifesciences was contacted and supported this work. The Academic Medical Center Amsterdam is the trial sponsor and will remain owner of all data and rights to publication. Edwards Lifesciences was not involved in design and conduct of the study, collection, management, analyses, interpretation of the data, preparation and review of the manuscript. The physician initiated study was supported by Edwards Lifesciences by supplying devices and finger cuffs. Edwards Lifesciences did not have to approve the manuscript; and had no decision to submit the manuscript for publication. Edwards Lifesciences read the manuscript before submission, but no publications restrictions apply. Author’s contributions Study conception and design: A.P.V., D.P.V., L.E.T., M.W.H. Data collection of the study: L.E.T. Data analyses: B.vd.S., L.E.T., M.L. Interpretation of findings: A.P.V., B.vd.S., M.L., J.S., D.P.V., L.E.T., M.L., M.W.H. Drafting of the manuscript: A.P.V., B.vd.S., D.P.V., J.S., L.E.T., M.L., M.W.H. Revising and final approval of the manuscript: A.P.V., B.vd.S., M.L., J.S., D.P.V., L.E.T., M.L., M.W.H. Aknowledgements S. Buddi, Edwards Lifesciences, did the part of analysis of Supplemental table 3. References Schenk J, van der Ven WH, Schuurmans J, Roerhorst S, Cherpanath TGV, Lagrand WK, et al. Definition and incidence of hypotension in intensive care unit patients, an international survey of the European Society of Intensive Care Medicine. J Crit Care. 2021;65:142–8. Poukkanen M, Wilkman E, Vaara ST, Pettila V, Kaukonen KM, Korhonen AM, et al. Hemodynamic variables and progression of acute kidney injury in critically ill patients with severe sepsis: data from the prospective observational FINNAKI study. Crit Care. 2013;17:R295. Maheshwari K, Nathanson BH, Munson SH, Khangulov V, Stevens M, Badani H, et al. The relationship between ICU hypotension and in-hospital mortality and morbidity in septic patients. Intensive Care Med. 2018;44:857–67. Varpula M, Tallgren M, Saukkonen K, Voipio-Pulkki LM. Hemodynamic variables related to outcome in septic shock. Intensive Care Med. Aug; 2005;31(8):1066–71. Leone M, Asfar P, Radermacher P, Vincent JL, Martin C. Optimizing mean arterial pressure in septic shock: a critical reappraisal of the literature. Crit Care. 2015;19:101. Vincent JL, Nielsen ND, Shapiro NI, Gerbasi ME, Grossman A, Doroff R, et al. Mean arterial pressure and mortality in patients with distributive shock: a retrospective analysis of the MIMIC-III database. Ann Intensive Care. 2018;8:107. Schneck E, Schulte D, Habig L, Ruhrmann S, Edinger F, Markmann M, et al. Hypotension Prediction Index based protocolized haemodynamic management reduces the incidence and duration of intraoperative hypotension in primary total hip arthroplasty: a single centre feasibility randomised blinded prospective interventional trial. J Clin Monit Comput. 2020;34:1149–58. Murabito P, Astuto M, Sanfilippo F, La Via L, Vasile F, Basile F et al. Proactive Management of Intraoperative Hypotension Reduces Biomarkers of Organ Injury and Oxidative Stress during Elective Non-Cardiac Surgery: A Pilot Randomized Controlled Trial. J Clin Med. 2022;11. Šribar A, Jurinjak IS, Almahariq H, Bandić I, Matošević J, Pejić J, Peršec J. Hypotension prediction index guided versus conventional goal directed therapy to reduce intraoperative hypotension during thoracic surgery: a randomized trial. BMC Anesthesiol. 2023;23:101. Maheshwari K, Shimada T, Yang D, Khanna S, Cywinski JB, Irefin SA, et al. Hypotension Prediction Index for Prevention of Hypotension during Moderate- to High-risk Noncardiac Surgery. Anesthesiology. 2020;133:1214–22. Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, et al. Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis. Anesthesiology. 2018;129:663–74. Wijnberge M, van der Ster BJP, Geerts BF, de Beer F, Beurskens C, Emal D, et al. Clinical performance of a machine-learning algorithm to predict intra-operative hypotension with noninvasive arterial pressure waveforms: A cohort study. Eur J Anaesthesiol. 2021;38:609–15. van der Ven WH, Terwindt LE, Risvanoglu N, Ie ELK, Wijnberge M, Veelo DP et al. Performance of a machine-learning algorithm to predict hypotension in mechanically ventilated patients with COVID-19 admitted to the intensive care unit: a cohort study. J Clin Monit Comput. 2021:1–9. Tsoumpa M, Kyttari A, Matiatou S, Tzoufi M, Griva P, Pikoulis E et al. The Use of the Hypotension Prediction Index Integrated in an Algorithm of Goal Directed Hemodynamic Treatment during Moderate and High-Risk Surgery. J Clin Med. 2021;10. Wijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, Berge P, et al. Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA. 2020;323:1052–60. Frassanito L, Giuri PP, Vassalli F, Piersanti A, Garcia MIM, Sonnino C, et al. Hypotension Prediction Index guided Goal Directed therapy and the amount of Hypotension during Major Gynaecologic Oncologic Surgery: a Randomized Controlled clinical Trial. J Clin Monit Comput. 2023;37:1081–93. Rhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M, Ferrer R, et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. Crit Care Med. 2017;45:486–552. Sessler DI, Bloomstone JA, Aronson S, Berry C, Gan TJ, Kellum JA, et al. Perioperative Quality Initiative consensus statement on intraoperative blood pressure, risk and outcomes for elective surgery. Br J Anaesth. 2019;122:563–74. Kleinman B, Powell S, Kumar P, Gardner Reed M. The Fast Flush Test Measures the Dynamic Response of the Entire Blood Pressure Monitoring System. Anesthesiology. 1992;77:1215–20. Maheshwari K, Khanna S, Bajracharya GR, Makarova N, Riter Q, Raza S, et al. A Randomized Trial of Continuous Noninvasive Blood Pressure Monitoring During Noncardiac Surgery. Anesth Analg. 2018;127:424–31. Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3:32–5. Martínez Pérez JA. Pérez Martin PS. [ROC curve]. Semergen. 2023;49:101821. Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39:561–77. Ozenne B, Subtil F, Maucort-Boulch D. The precision–recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases. J Clin Epidemiol. 2015;68:855–9. DW Hosmer SL. Applied Logistic Regression. 2nd Ed ed. New York, NY John Wiley and Sons; 2000. De Backer D, Cecconi M, Lipman J, Machado F, Myatra SN, Ostermann M, et al. Challenges in the management of septic shock: a narrative review. Intensive Care Med. 2019;45:420–33. van der Ven WH, Veelo DP, Wijnberge M, van der Ster BJP, Vlaar APJ, Geerts BF. One of the first validations of an artificial intelligence algorithm for clinical use: The impact on intraoperative hypotension prediction and clinical decision-making. Surgery. 2021;169:1300–3. Enevoldsen J, Vistisen ST. Performance of the Hypotension Prediction Index May Be Overestimated Due to Selection Bias. Anesthesiology. 2022;137:283–9. Davies SJ, Vistisen ST, Jian Z, Hatib F, Scheeren TWL. Ability of an Arterial Waveform Analysis-Derived Hypotension Prediction Index to Predict Future Hypotensive Events in Surgical Patients. Anesth Analg. 2020;130:352–9. Maheshwari K, Buddi S, Jian Z, Settels J, Shimada T, Cohen B, et al. Performance of the Hypotension Prediction Index with non-invasive arterial pressure waveforms in non-cardiac surgical patients. J Clin Monit Comput. 2021;35:71–8. Davies SJ, Sessler DI, Jian Z, Fleming NW, Mythen M, Maheshwari K, Veelo DP, Vlaar AP, Settels J, Scheeren T, vd Ster BJP, Sander M, Cannesson M. F.Hatib, Comparison of differences in cohort (forwards) and case control (backwards) methodological approaches for validation of the hypotension prediction index, Anesthesiology. 2024. Additional Declarations No competing interests reported. Supplementary Files suplFig1APR.png suplFig1BROC.png suplFig2AROCpostcapu.png suplFig2BPRpostcapu.png suplFig2CROCSAB.png suplFig2DPRSAB.png suplFig2EROCcardiac.png suplFig2FPRcardiac.png suplFig2GROCdistributief.png PHYSICHPIsupplementstablesandappendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4169157","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285677347,"identity":"805838e2-e88d-43a7-8902-365f5354620a","order_by":0,"name":"Lotte E. Terwindt","email":"","orcid":"","institution":"University of Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Lotte","middleName":"E.","lastName":"Terwindt","suffix":""},{"id":285677348,"identity":"0a46b9ac-304b-4dbd-9c68-2a6fca196666","order_by":1,"name":"Denise P. 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Ster","email":"","orcid":"","institution":"University of Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Björn","middleName":"J.P.","lastName":"Ster","suffix":""}],"badges":[],"createdAt":"2024-03-26 10:38:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4169157/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4169157/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54106753,"identity":"bc5d620e-ea1a-4e18-ae5d-538be1dbb83a","added_by":"auto","created_at":"2024-04-04 17:24:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9670,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient inclusion\u003c/p\u003e","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-4169157/v1/19dcd7f0dc4ad4c7ddd417a3.png"},{"id":54106756,"identity":"e657cf5d-35ed-465f-a23d-fad936096e1d","added_by":"auto","created_at":"2024-04-04 17:24:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76591,"visible":true,"origin":"","legend":"\u003cp\u003eROC and PR curve of HPI validation protocol\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubscript\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2A:\u003c/strong\u003e ROC curve of HPI validation protocol\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2B\u003c/strong\u003e PR curve of HPI validation protocol\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-4169157/v1/e650e2d57e7c691c25a2c4ec.png"},{"id":54106761,"identity":"883da7f8-ab09-43ad-ae4f-f3891bd3171b","added_by":"auto","created_at":"2024-04-04 17:24:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eSensitivity against time to hypotension\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubscript\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing an HPI alarm threshold of 85\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB \u003c/strong\u003eTime from first HPI alarm (HPI 85) to hypotension event\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubscript\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDistribution of time to hypotension, using an alarm threshold of 85. For the assessment, the alarm is defined as the first HPI value above threshold in the prediction window. Bars include the higher edge value end exclude the lower edge value. The bar for a time­to­hypotension higher than 15 min indicates the amount of hypotensive events that were preceded by 15 minutes of non­stop HPI alarms. The total count is 982. The median time to hypotension [IQR] was 2.7 [1 to 6.3] minutes for an HPI threshold of 85.\u003c/p\u003e","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-4169157/v1/09300892aecbd89b9299c4e1.png"},{"id":54106758,"identity":"f19a4a47-baca-4511-9c51-466484594900","added_by":"auto","created_at":"2024-04-04 17:24:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18710,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003erade-off between PPV and Time to Event at different HPI thresholds.\u003c/p\u003e\n\u003cp\u003eTable of contents supplements\u003c/p\u003e","description":"","filename":"Fig4TradeoffbetweenPPVandTimetoEvent.png","url":"https://assets-eu.researchsquare.com/files/rs-4169157/v1/b5463b982d669dcb894bda66.png"},{"id":59430423,"identity":"7a941eb4-461c-4ef4-ba5a-74f5fb6121b0","added_by":"auto","created_at":"2024-07-01 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17:24:59","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":51193,"visible":true,"origin":"","legend":"","description":"","filename":"PHYSICHPIsupplementstablesandappendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4169157/v1/37197e2477b420b14d6a754b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validation and clinical applicability of the Hypotension Prediction Index in a general ICU population: a prospective observational cohort study Study acronym Prediction of Hemodynamic Instability in Patients Admitted to the ICU; the PHYSIC study","fulltext":[{"header":"Key Points","content":"\u003cul\u003e\n \u003cli\u003eThis is the first validation of HPI in a broad subset of ICU patients\u0026nbsp;using the forward tumbling method including all data points.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHPI is highly accurate in predicting hypotension in ICU patients, and allows for the initiation of a preventive rather than reactive treatment of hypotension up to three minutes before its actual onset.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe HPI model showed excellent performance regardless of diagnoses at ICU admission.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe average time between alarm (HPI\u0026ge;85) and the onset of hypotension is relatively short, which might affect the applicability and added value in an ICU setting.\u0026nbsp;A lower HPI threshold (75-85) would facilitate a longer time to event, and might prove to be more applicable in daily ICU practice\u003cstrong\u003e.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eHypotensive events during admission on the intensive care unit (ICU) occur frequently and treatment is challenging.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Both the depth and duration of hypotension are associated with adverse outcomes including mortality in ICU patients. [\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Pro-active hypotension treatment with the use of a predictive alarm could potentially reduce the number and severity of hypotensive events, by providing the opportunity to intervene before hypotension occurs and could potentially reduce the number and severity of hypotensive events, by providing the opportunity to intervene before hypotension occurs. [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe Hypotension Prediction Index (HPI) is a machine-learning derived algorithm that alarms for impending hypotension, with values ranging from 0 to 100. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] The algorithm was trained on arterial blood pressure (BP) waveform data of 1334 surgical and ICU patients to calculate the probability of impending hypotension. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Several studies, using different analysis methods, showed a hypotension sensitivity and specificity prediction of 80\u0026ndash;90%, with the time of alarm onset to hypotension, predicted by HPI, ranging from 4\u0026ndash;15 minutes. [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe clinical performance of HPI has been demonstrated in multiple samples of surgical patients.[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] However, data on predictive and clinical performance of HPI in ICU patients is lacking, as only one external validation study has been performed in a small group of ICU patients with COVID-19. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Assessment of the performance of HPI in a diverse ICU patient population is warranted to assess its utility in this setting. Additionally, logistics in the operating room differs from the ICU as nurses and clinicians are not continuously at bedside, potentially delaying treatment initiation. Therefore, it is important to assess whether the average time between prediction and actual onset of hypotension allows for implementation of HPI in the ICU setting.\u003c/p\u003e \u003cp\u003eIn this prospective observational study, we assessed the applicability of HPI in the ICU. The primary objective was to assess the overall performance of HPI to predict hypotension in general ICU patients using the forward tumbling method. Hypotension was defined as a mean arterial pressure (MAP) below 65 mmHg for at least one minute. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSecondary objectives included estimation of the time to hypotensive events at different HPI alarm thresholds, performance metrics at a clinical relevant HPI threshold of 85, change in the average sensitivity of HPI-85 in the minutes preceding a hypotensive event, differences in performance at various prediction time windows and the discriminative performance of HPI in different subgroups based on patient category.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis was a single center, prospective, observational study in patients admitted to the mixed surgical and non-surgical ICU of a tertiary academic medical centre in Amsterdam, the Netherlands. This study was approved by the Medical Ethical Committee of Amsterdam UMC location AMC, the Netherlands in May 2018 (Source ID: W18_142#18.176) and registered with the Netherlands Trial Register (NTR7349). Data were collected from the 9th of September 2018 until the 30th of May 2019. The study was conducted in compliance with the Declaration of Helsinki (Fortaleza, 2013), the Dutch Medical Research Involving Human Subjects Act and Good Clinical Practice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003ePatients\u0026thinsp;\u0026ge;\u0026thinsp;18 years, newly admitted to the ICU, either during the day or evening/night period and who received an arterial line as part of standard care, were eligible for inclusion. Exclusion criteria were: an inability to measure continuous BP data with an arterial line, expected admission time\u0026thinsp;\u0026lt;\u0026thinsp;8 hours, controlled hypotension or hypertension with a target MAP lower or higher than 65 mmHg, severe arrhythmias and logistic difficulties (e.g. transfer to another hospital). To obtain sufficient data for validation, we intended to collect data for an expected minimum of eight consecutive hours.\u003c/p\u003e \u003cp\u003eTrained and delegated members of the study team screened patients. Written informed or deferred consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements and signal quality\u003c/h2\u003e \u003cp\u003eContinuous BP data were collected with a hemodynamic monitor (the EV1000, Edwards Lifesciences LTD, Irvine, CA, USA) using the arterial line system of patients with Flotrac sensors. The monitor was blinded for the treatment team and was solely used for study data collection. Hemodynamic variables and BP data were derived directly from arterial waveform analyses to the EV1000 monitor. Intra-arterial BP was measured with a five French cannula in the radial artery or if placement in the radial artery was not possible, the brachial or femoral artery. Before starting measurements, the transducer of the arterial line was placed at the level of the right atrium and zeroed. We frequently performed fast flush tests to ensure the system was neither over- nor underdamped.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Hemodynamic data were stored on an internal disk. Anonymized data files were copied to a USB flash drive and stored on a secure hospital server; only accessible to members of the study team.\u003c/p\u003e \u003cp\u003ePatient characteristics (age, weight, height, sex, intoxications, medical history, (home) medication, ventilator settings, reason for ICU admission and sequential organ failure assessment score (SOFA)) were all extracted from the electronic patient database (2018, Epic Systems Corporation, Verona, Wisconsin, USA). All de-identified data were entered into a good clinical practice compliant database (Castor EDC\u0026trade;, version 2019.1.5) and handled according to the General Data Protection Regulation of May 2018. A 10% check was performed after data entry in Castor EDC\u0026trade; and less than three percent errors were found which was considered satisfactory.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDefinitions and analyses\u003c/h2\u003e \u003cp\u003e The dataset was reviewed for accuracy, missing values, and outliers. Distribution of data was assessed using histograms, QQ-plots and boxplots. Categorical data were described as frequencies (percentages) and group differences were assessed with a Pearson\u0026rsquo;s Chi-squared (if frequencies were \u0026le;\u0026thinsp;5) or a Fisher\u0026rsquo;s exact test. Continuous variables were presented as mean with standard deviation (SD) or median with first and third quartile [Q1-Q3] when appropriate. Differences in continuous data were calculated using t-tests, Kruskal Wallis or Mann-Whitney-U depending on distribution. Statistical significance was assumed at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eA hypotensive event was defined as an absolute MAP value below 65 mmHg for at least one minute. For each patient, monitoring time, number of hypotensive events, time spent in hypotension, area under the threshold and the time weighted average (TWA) of hypotension were determined. The combination of depth and duration of hypotension was expressed in TWA. The TWA was calculated by dividing the cumulative sum of the area under the threshold (AUT) of hypotension by the total duration of the measurement period. The units for AUT are mmHg*min and the units for TWA are mmHg. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePrimary objective\u003c/h2\u003e \u003cp\u003eThe primary objective was to assess the performance of HPI in predicting hypotension in a general ICU population. The predictive ability of HPI was evaluated at each threshold (0-100) using a forward tumbling analysis similar to Wijnberge et al. and van der Ven et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] A grey zone (MAP 65\u0026ndash;75 zone excluded for analysis) has not been used in this validation method. The validation was divided into three stages: 1) data preprocessing, 2) labeling of data according to definitions of (no-) alarms and (non-) hypotension and 3) calculation of performance metrics (Appendix A).\u003c/p\u003e \u003cp\u003eData were labelled with a tumbling window approach. After exceeding the studied HPI threshold, the next interval of 20 minutes was screened for the onset of a hypotensive event. The time window was then \u0026rsquo;tumbled\u0026rsquo; or \u0026rsquo;flipped\u0026rsquo; ahead in time, so windows did not overlap. Data were sequentially labelled from the start to the end of the data timeline of a patient. As long as no alarm was encountered, each past 20 minute window was labelled based on the occurrence of hypotension in that window. Upon an alarm, a new 20 minute window starting from the alarm was forced. Every next window only started once hypotension had resolved. A subsequent hypotensive episode, as well as an HPI alarm only counts as two separate events when respectively the MAP or the HPI normalized for at least one minute.\u003c/p\u003e \u003cp\u003eTimeframes were labelled as true positive (TP), false positive (FP), true negative (TN), or false negative (FN). (Supplemental table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e., Appendix B) To correct for overrepresentation of TNs, a maximum of one non-hypotensive event (MAP\u0026thinsp;\u0026gt;\u0026thinsp;65 mmHg) was maintained per 20-minute time interval after a HPI alarm. Interventions were automatically assessed by excluding measurements that were most likely influenced by clinical interventions, such as administration of vasopressors or application of positional changes resulting in a rapid rise in BP. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for HPI were calculated, ranging from 0 to 100 in steps of five. Furthermore, Youden\u0026rsquo;s J statistic was calculated to determine the statistically optimal HPI threshold. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Discriminative performance was further evaluated using the Receiver Operating Characteristic (ROC) curve and precision recall (PR) curve. [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] (Appendix C) For both the area under the ROC and PR curve, values between 0.7 to 0.8 were considered acceptable, while values above 0.8 were considered excellent.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSecondary objectives\u003c/h2\u003e \u003cp\u003eSecondary objectives included estimation of the time to hypotensive events at different HPI alarm thresholds, performance metrics at a clinical relevant HPI threshold of 85, change in the average sensitivity of HPI-85 in the 1 to 20 minute(s) preceding a hypotensive event (in steps of one minute), differences in performance at various prediction time windows and the discriminative performance of HPI in different subgroups based on patient category.\u003c/p\u003e \u003cp\u003eMedian time-to-event and event rate were determined for every HPI threshold. The event rate is computed as the number of hypotensive events that follow an HPI alarm divided by total alarms.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003ePerformance at HPI 85\u003c/h2\u003e \u003cp\u003eEarlier studies employed 20 minute prediction windows, and showed that most hypotensive events followed HPI alarms within several minutes. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Hence, 20 minute timeframes might include late onset hypotensive events not necessarily associated with concurrent HPI values. To this extent, to assess the effect of shrinking the prediction window (to 5, 10, and 15 minutes) on the sensitivity and specificity of HPI-85 was attempted.\u003c/p\u003e \u003cp\u003eIn sensitivity analyses, HPI performance was evaluated in clinically relevant subgroups of patients that were stratified based on the diagnoses at time of their ICU admission. Subgroups analysed were: patients with cardiogenic shock, distributive shock, admission post cardiothoracic surgery and admission due to a subarachnoid hemorrhage. Data analyses were performed with IBM SPSS\u0026reg; software (version 25.0) and MATLAB (The MathWorks, Inc., Natick, MA, USA) version 9.5.0 (R2018b).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eA total of 514 patients meeting the inclusion criteria were screened and 499 patients were included in the data analyses. (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e1\u003c/span\u003e) Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the patients\u0026rsquo; characteristics. The mean age was 61 years (14), most patients were male (66%), the median SOFA score was 10 [\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]) and the most common reason for ICU admission was post-surgery (43%). Most patients required mechanical ventilation (72%) and received vasoactive medication (61%). The majority of measurements (61%) were obtained during daytime. The median monitoring time per patient was 7 hours and 21 minutes (441 minutes [411\u0026ndash; 962]). The sample contained 759 hypotensive events, with a median of six [\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] events per patient. A patient showed an average of one event per 73 minutes for a median duration of 52 minutes [5-170] per event, resulting in a median TWA of hypotension of 0.3 mmHg [0.03-1.0]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline patient and monitoring characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, mean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e61 (14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patients older than 65 years, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg), mean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(19.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm), mean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e174 (9.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, mean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score, median [Q1-Q3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(8\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVasoactive medication during measurements, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMeasurement details\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonitoring time per patient (minutes), median [Q1\u0026shy;Q3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e441 [411\u0026ndash;962]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of daytime measurements, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of night\u0026shy;time measurements, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReason of ICU admission\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory failure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e57 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e82 (16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubarachnoid haemorrhage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e51 (10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac shock/other cardiac, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative after surgery, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiothoracic surgery, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssigned shock groups\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiogenic shock, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistributive shock, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypovolemic shock, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstructive shock, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombination type of shock, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonshock classification, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaemodynamic data of patients with hypotension MAP\u0026thinsp;\u0026lt;\u0026thinsp;65 mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWA per patient (mmHg), median [Q1-Q3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.03-1.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUT MAP 65 mmHg per patient mmHg.min, median [Q1-Q3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of events per patients, median [Q1-Q3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal duration of events per patient (min), median [Q1-Q3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[5-170]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal percentage duration of measurement in hypotension (%), median [Q1-Q3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.7\u0026ndash;29.1]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eStatistic presented as mean (standard deviation), median [first quartile, third quartile], or number of patients (%). Abbreviations: MAP, mean arterial pressure; BMI, body mass index; SOFA, sequential organ failure assessment; TWA, time weighted average.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrimary objective; validation and performance of HPI in the ICU\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the discriminatory ability of HPI, with an area under the PR curve of 0.95 and the area under the ROC curve of 0.97. The optimal statistical threshold for the forward tumbling validation method was found at HPI 95 for all statistical optimums: Youden Index (0.87), and minimal difference between sensitivity (0.97) and specificity (0.89). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays sensitivity, specificity, Youden\u0026rsquo;s J statistic, PPV, NPV, time to event, and event rate for HPI thresholds between 0 and 100 at incremental intervals of five.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ea\u003c/b\u003e HPI, overview thresholds*\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPI threshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYouden\u0026rsquo;s J statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eEvent rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eHPI with hypotensive event\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eTotal HPI alarms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e23098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e22269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e21778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e21247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e20744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e20219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e19632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e18907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e18220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e17806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e17408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e17051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.76]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e16635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e16172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e15754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e15379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e11000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e14930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e10925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e14461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e10690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e13657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e9743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e11397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eAbbreviations: \u003cem\u003eSe\u003c/em\u003e, sensitivity; \u003cem\u003eSp\u003c/em\u003e, Specificity; \u003cem\u003ePPV\u003c/em\u003e, positive predictive value; \u003cem\u003eNPV\u003c/em\u003e, negative predictive value.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eF1 (score: 0.94), Youden, Min. diff. Se en Sp, Event rate (detections / alarms\u0026thinsp;=\u0026thinsp;True positives / (True positives\u0026thinsp;+\u0026thinsp;False positives)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eHPI with hypotensive event; all detections of HPI followed by an hypotensive event.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eTotal HPI alarms: An alarm is defined by a combination of an HPI value and the alarm threshold.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eData presented as median [Q1-Q3]\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e*20 minutes interval scanrange\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eb\u003c/b\u003e: Time-to-event Analysis, HPI thresholds\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPI threshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMedian Time to Event (sec)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[80\u0026ndash;440]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[80\u0026ndash;440]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[80\u0026ndash;440]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[80\u0026ndash;440]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[80\u0026ndash;440]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[80\u0026ndash;440]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[80\u0026ndash;440]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[80\u0026ndash;420]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[80\u0026ndash;420]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[80\u0026ndash;420]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[80\u0026ndash;420]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[80\u0026ndash;420]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[80\u0026ndash;420]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[60\u0026ndash;420]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[60\u0026ndash;400]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[60\u0026ndash;400]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[60\u0026ndash;400]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[60\u0026ndash;380]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[60\u0026ndash;360]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[60\u0026ndash;320]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e* True positive samples are used for estimating time-to-event. HPI range for all TPs at a given threshold is provided for clarity\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSecondary objective; Time to event and performance at HPI 85\u003c/h2\u003e \u003cp\u003eAn increase in HPI threshold from 65 to 95 resulted in a decrease of average time between the alarm and onset of hypotension. When employing an HPI threshold of 85, the median time to hypotension was 2.7 [1 to 6.3] minutes. No alarm preceded in 35 of the 759 (4.6%) hypotensive events. Sensitivity increased from 75\u0026ndash;80% in the time window 30 to 10 minutes before the onset of hypotension, thereafter, sensitivity increased more rapidly. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) As described in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the increase in HPI value itself was proportional to the decrease in time to event and increase in hypotension occurrence (event rate). This trade-off between HPI threshold, positive predictive value and time-to-event is visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSecondary objective; changing the prediction window duration\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e represents the performance metrics of different prediction window durations for a HPI threshold of 85. For prediction window durations of 5 and 20 minutes, the area under the PR curve was 0.69 and 0.95, respectively. An increase in the prediction window resulted in an increase in PPV, but a decrease of specificity (Supplemental Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance metrics for different prediction windows, supplemented with a leading neutral buffer to a total of 20 minutes, for an HPI threshold of 85.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrediction window\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5 min\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e10 min\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e15 min\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e20 min\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUROC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUCPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: PPV, positive predictive value; NPV, negative predictive value; AUROC, area under the ROC curve; AUCPR, area under the Precision Recall curve.\u003c/p\u003e \u003cp\u003eLeading neutral buffer: Every HPI value is regarded to cast a prediction over the succeeding 15 minutes, as the HPI model was developed with waveforms up until 15 minutes prior to onset of hypotension. Therefore, a sliding window with a duration of 15 minutes was used to assess HPI performance. The\u003c/p\u003e \u003cp\u003ecorrectness of every alarm is based on the occurrence of hypotension in this window. A leading neutral buffer of 5 minutes was used, so that alarms with a time to hypotension of 15 to 20 minutes were not labelled as a FP.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSecondary objective; HPI performance in patient category subgroups\u003c/h2\u003e \u003cp\u003eSubgroup performance with an alarm threshold of 85, including PR and ROC curves are presented for each subgroup analysis. (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplemental Fig.\u0026nbsp;2) In line with the overall population, HPI showed excellent performance in each subgroup of ICU patients. Only in patients with a subarachnoid hemorrhage a lower, but still an excellent area under the PR curve was found (0.85).\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003e Performance per subgroup, with an HPI alarm threshold of 85.\u003c/p\u003e\n\u003ctable style=\"border: none;margin-left:2.0pt;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0cm;height: 13.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family:\"Calibri\",sans-serif;'\u003eSubgroup\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 93pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0cm;height: 13.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Calibri\",sans-serif;'\u003eCAPU Admission\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0cm;height: 13.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Calibri\",sans-serif;'\u003eSAH admission\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 102pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0cm;height: 13.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Calibri\",sans-serif;'\u003eCardiogenic shock\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 105pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0cm;height: 13.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Calibri\",sans-serif;'\u003eDistributive shock\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0cm;padding: 0cm;height: 13.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-family:\"Calibri\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56pt;padding: 0cm;height: 11.35pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family:\"Calibri\",sans-serif;'\u003eSensitivity\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0cm;height: 11.35pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;line-height:11.35pt;'\u003e\u003cspan style='font-family:\"Calibri\",sans-serif;'\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid rgb(255, 238, 238);padding: 0cm;height: 11.35pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;line-height:11.35pt;'\u003e\u003cspan style='font-family:\"Calibri\",sans-serif;'\u003eNo\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0cm;height: 11.35pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;line-height:11.35pt;'\u003e\u003cspan style='font-family:\"Calibri\",sans-serif;'\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid rgb(255, 238, 238);padding: 0cm;height: 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11.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;'\u003eAUROC\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36pt;padding: 0cm;height: 11.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;'\u003e0.967\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57pt;background: rgb(255, 238, 238);padding: 0cm;height: 11.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;color:black;'\u003e0.974\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;padding: 0cm;height: 11.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;'\u003e0.988\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48pt;background: rgb(255, 238, 238);padding: 0cm;height: 11.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;color:black;'\u003e0.971\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;padding: 0cm;height: 11.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;'\u003e0.948\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65pt;background: rgb(255, 238, 238);padding: 0cm;height: 11.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;color:black;'\u003e0.976\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36pt;padding: 0cm;height: 11.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;'\u003e0.953\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70pt;background: rgb(255, 238, 238);padding: 0cm;height: 11.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;color:black;'\u003e0.976\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0cm;padding: 0cm;height: 11.95pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-family:\"Calibri\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0cm;height: 14.85pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;'\u003eAUCPR\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0cm;height: 14.85pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;'\u003e0.957\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;background: rgb(255, 238, 238);padding: 0cm;height: 14.85pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;color:black;'\u003e0.928\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0cm;height: 14.85pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;'\u003e0.846\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;background: rgb(255, 238, 238);padding: 0cm;height: 14.85pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;color:black;'\u003e0.953\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0cm;height: 14.85pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;'\u003e0.962\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;background: rgb(255, 238, 238);padding: 0cm;height: 14.85pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;color:black;'\u003e0.950\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0cm;height: 14.85pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;'\u003e0.940\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;background: rgb(255, 238, 238);padding: 0cm;height: 14.85pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;margin-left:6.0pt;'\u003e\u003cspan style='font-family: \"Calibri\",sans-serif;color:black;'\u003e0.953\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0cm;padding: 0cm;height: 14.85pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0cm;font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-family:\"Calibri\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: \u003cem\u003ePPV\u003c/em\u003e, positive predictive value; \u003cem\u003eNPV\u003c/em\u003e, negative predictive value; \u003cem\u003eAUROC\u003c/em\u003e, area under the ROC curve; \u003cem\u003eAUCPR\u003c/em\u003e, area under the Precision Recall curve; \u003cem\u003eCAPU\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003ecardiothoracic surgery; \u003cem\u003eSAH\u003c/em\u003e, subarachnoid haemorrhage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis first study of the performance of HPI in a general ICU population showed excellent performance in predicting hypotension. Moreover, the discriminative ability of the HPI algorithm in this cohort can be considered adequate. Time to hypotensive events increased by decreasing the HPI alarming threshold, and a decrease in the prediction window resulted in an increase in PPV, but a decrease in specificity. The HPI model showed excellent performance in all subgroups of patients.\u003c/p\u003e \u003cp\u003eWhile previous studies already showed excellent performance of HPI to predict hypotension in a smaller group of ICU COVID-19 and perioperative patients, [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] insight in the performance and applicability of HPI in the general ICU population is currently lacking. Therefore, we performed a study in a large group of ICU patients with different diagnoses and a wide variety in possible underlying causes of hypotension. In this sample of a general ICU population, we aimed to assess topics that were deemed relevant to be assessed before considering the application of HPI in such a high complex setting. Thereto, we assessed the overall accuracy of HPI, the probability of hypotension onset at different HPI alarm thresholds and the time between the onset of an alarm and the actual occurrence of hypotension. We furthermore evaluated whether the performance of HPI differed in various patient subgroups.\u003c/p\u003e \u003cp\u003eOur findings indicate that HPI is highly accurate in predicting hypotension in the general ICU population. As expected, accuracy of HPI increased both when alarms occurred closer to the onset of hypotension, and when higher alarm thresholds were employed. For adequate clinical applicability, an optimum between correct prediction of hypotensive events and the prevention of unnecessary treatments is essential. This optimum is normally found by assessing the balance between sensitivity and specificity of a prediction model. For all HPI thresholds, sensitivity was \u0026gt;\u0026thinsp;0.9, while specificity steadily increased with HPI thresholds, with an optimal statistical sensitivity and specificity trade-off calculated at a threshold of HPI 95. However, in a clinical environment, clinicians ultimately desire time-series based predictive alarms to not only provide accurate, but also timely notifications of upcoming events. This emphasizes the necessity to additionally assess the trade-off between the positive predictive value and time-to-event at different HPI alarming thresholds.\u003c/p\u003e \u003cp\u003eIn previous research [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], an alarm threshold of 85 was used to initiate treatment in a perioperative setting. However, in contrast to this setting, the time between onset of hypotension and initiation of direct treatment is likely to be longer in an ICU setting, due to logistical challenges, as physicians and nurses oversee more than one patient. In this context, the average treatment window of three minutes found in this study would still facilitate a more timely treatment, but might prove too small to also allow for the actual prevention of hypotensive events. If, for example, a PPV of 80% and a specificity of 70% would be considered as the lower bound to initiate preventative treatment, employing a HPI alarm of 75 would result in additional response time, without neglecting these boundaries. Ultimately, these boundaries and the corresponding alarm value should be at the discretion of the treating clinician. Moreover, these boundaries might even have to differ between patients to provide individualized hemodynamic care.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] To fully understand its impact on outcomes and logistical aspects, follow-up studies are essential.\u003c/p\u003e \u003cp\u003eOur statistically optimal HPI threshold of 95 differs in comparison to previous findings, which might be explained by the inclusion of a larger cohort of general ICU patients and due to the utilization of the forward tumbling validation method in this study, similar to the approach by Wijnberge et al. and van der Ven et al.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Selection bias and the specific validation methods in earlier studies could potentially explain a relevant proportion of the differences in predictive ability. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] When interpreting earlier studies, it is important to realize a number of confounding factors. In general, interpreting results of HPI validations (internal and external) comes with several drawbacks because of dependency on type of patients included, the MAP threshold labelled as hypotension, the role of the underlying cause of hypotension (preload, afterload, contractility), the role of clinical manipulation or decision making and type of validation method (forward or backward in combination with sliding or tumbling). With the forward tumbling method as described in this study, each hypotensive event is unique. In addition, this validation method seems most appropriate with regards to the intended clinical use of HPI, only the onsets of alarms were labelled, indicating the intended initiation of proactive treatment. Each individual alarm was assessed using non-overlapping time windows, in contrast to the forward sliding method where each individual prediction is annotated first with an increased risk of overlap between HPI and hypotensive events, thereby avoiding overestimation of results (sensitivity/specificity). This feature strengthens the generalizability of the results to the clinical (ICU) setting. However, important to note is that an element was added to the definition of an alarm, which may have clinical consequences. In this forward tumbling protocol, a minimal duration of one minute of HPI values above alarm threshold was added to the alarm definition. This constraint raises alarm criteria and reasonably reduces false positives. This would only be justified as long as the clinical protocol of HPI use would also incorporate this minimum duration. The downside of this constraint is the reduction of timeliness of the eventual alarm. In addition, this protocol does not consider that user behaviour may change when HPI decreases to subthreshold levels after the initial alarm, which could make the clinician cancel the proactive treatment. Ultimately, the responsibility for the appropriate clinical use of HPI remains with the clinician and the impact of its effectiveness is highly dependent on the behaviour of end-users.\u003c/p\u003e \u003cp\u003eThe HPI model showed excellent performance in all subgroups of patients. For patients with a distributive shock, cardiogenic shock and patients admitted after cardiothoracic surgery, the HPI model showed the best trade\u0026shy;off between sensitivity and PPV. The HPI showed a slightly smaller, but still excellent performance in patients admitted due to a subarachnoid hemorrhage. This difference might in part be explained by the lower prevalence of hypotension in this population, combined with the underlying pathophysiology. This patient category commonly has an elevated BP due to increased intracranial pressure, although patients with controlled hypertension with a target MAP higher than 65 mmHg were excluded.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and strengths\u003c/h2\u003e \u003cp\u003eThis study focused on patients with a target MAP of 65 mmHg. Validation of the algorithm for higher or lower MAP values was not done in this study, but will be clinically relevant if this algorithm is to be applied in clinical practice in the future.\u003c/p\u003e \u003cp\u003eRegistration of clinical interventions or treatments by personnel around events were missing due the observational character of this study. This could affect the prediction and rate of occurrence of an event after an alarm has been triggered. However, BP data during interventions were therefore not included for analysis.\u003c/p\u003e \u003cp\u003eDuration of measurements were eight hours, while median length of stay in the ICU was much longer in most patients. In this study, we included most patients immediately upon their admission to the ICU and there may have been more hemodynamic instability during these measurements compared to measurements that recorded the entire admission period.\u003c/p\u003e \u003cp\u003eIn this study, we primarily treated each minute of HPI surpassing the threshold as an alarm. Contrarily, when inspecting specific HPI values in a certain range: e.g. 45\u0026thinsp;\u0026lt;\u0026thinsp;HPI\u0026thinsp;\u0026lt;\u0026thinsp;50 (Supplemental table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), a decrease in time to event is observed with increasing HPI. The similarity in time to event (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e) is most likely explained by HPI rapidly increasing when hypotension is imminent, as the thresholding approach does not take into account HPI values as long as they surpass the threshold. Even though, this 'binning' of HPI values is clinically less intuitive compared to the thresholding approach we added both for clarity and transparency.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, HPI showed excellent performance in the prediction of hypotension in a general ICU population, without differences between subgroups. However, the average time between alarm (HPI\u0026thinsp;\u0026ge;\u0026thinsp;85) and the onset of hypotension is relatively short, which might affect its applicability and added value in an ICU setting. A lower HPI threshold (75\u0026ndash;85) would facilitate a longer time to event, and might prove to be more applicable in daily ICU practice. Intervention studies are necessary to evaluate implementation barriers and assess the effect on relevant patient outcomes.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eAUT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Area under the threshold\u003c/p\u003e\n\u003cp\u003eBP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;False negative\u003c/p\u003e\n\u003cp\u003eFP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;False positive\u003c/p\u003e\n\u003cp\u003eHPI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hypotension prediction index\u003c/p\u003e\n\u003cp\u003eICU\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intensive care unit\u003c/p\u003e\n\u003cp\u003eMAP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mean arterial pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNPV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Negative predictive value\u003c/p\u003e\n\u003cp\u003ePPV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Positive predictive value\u003c/p\u003e\n\u003cp\u003ePR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Precision recall\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Receiver operator characteristic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Standard deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSOFA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sequential organ failure assessment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;True negative\u003c/p\u003e\n\u003cp\u003eTP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;True positive\u003c/p\u003e\n\u003cp\u003eTWA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Time weighted average\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Medical Ethical Committee of Amsterdam UMC location AMC, the Netherlands in May 2018 (Source ID: W18_142#18.176).\u0026nbsp;This study was registered with the Netherlands Trial Register (NTR7349). The study was conducted in compliance with the Declaration of Helsinki (Fortaleza, 2013), the Dutch Medical Research Involving Human Subjects Act (WMO) and Good Clinical Practice (GCP).\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAPV and LT had full access to all the data in the study and takes responsibility for the integrity and accuracy of analyses of the data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe Department of Anesthesiology of the Academic Medical Center received financial support for this project from Edwards Lifesciences. The investigators (APV, DPV) have potential conflicts of interest involving the work under review since they received consultancy fees from Edwards Lifesciences. None of the investigators of the AMC have any form of (in) direct ownership in the software or hardware of Edwards and/or subject of this study. Also no rights or claims to rights exist that might lead to financial gains for any of the authors or the AMC as an institution.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eAfter design of the trial, Edwards Lifesciences was contacted and supported this work. The Academic Medical Center Amsterdam is the trial sponsor and will remain owner of all data and rights to publication. Edwards Lifesciences was not involved in design and conduct of the study, collection, management, analyses, interpretation of the data, preparation and review of the manuscript. The physician initiated study was supported by Edwards Lifesciences by supplying devices and finger cuffs. Edwards Lifesciences did not have to approve the manuscript; and had no decision to submit the manuscript for publication. Edwards Lifesciences read the manuscript before submission, but no publications restrictions apply.\u003c/p\u003e\n\u003ch2\u003eAuthor\u0026rsquo;s contributions\u003c/h2\u003e\n\u003cp\u003eStudy conception and design: A.P.V., D.P.V., L.E.T., M.W.H.\u003c/p\u003e\n\u003cp\u003eData collection of the study: L.E.T.\u003c/p\u003e\n\u003cp\u003eData analyses: B.vd.S., L.E.T., M.L.\u003c/p\u003e\n\u003cp\u003eInterpretation of findings: A.P.V., B.vd.S., M.L., J.S., D.P.V., L.E.T., M.L., M.W.H.\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript: A.P.V., B.vd.S., D.P.V., J.S., L.E.T., M.L., M.W.H.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRevising and final approval of the manuscript: A.P.V., B.vd.S., M.L., J.S., D.P.V., L.E.T., M.L., M.W.H.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eS. Buddi, Edwards Lifesciences, did the part of analysis of Supplemental table 3.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchenk J, van der Ven WH, Schuurmans J, Roerhorst S, Cherpanath TGV, Lagrand WK, et al. Definition and incidence of hypotension in intensive care unit patients, an international survey of the European Society of Intensive Care Medicine. J Crit Care. 2021;65:142\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoukkanen M, Wilkman E, Vaara ST, Pettila V, Kaukonen KM, Korhonen AM, et al. Hemodynamic variables and progression of acute kidney injury in critically ill patients with severe sepsis: data from the prospective observational FINNAKI study. Crit Care. 2013;17:R295.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaheshwari K, Nathanson BH, Munson SH, Khangulov V, Stevens M, Badani H, et al. The relationship between ICU hypotension and in-hospital mortality and morbidity in septic patients. Intensive Care Med. 2018;44:857\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarpula M, Tallgren M, Saukkonen K, Voipio-Pulkki LM. Hemodynamic variables related to outcome in septic shock. Intensive Care Med. Aug; 2005;31(8):1066\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeone M, Asfar P, Radermacher P, Vincent JL, Martin C. Optimizing mean arterial pressure in septic shock: a critical reappraisal of the literature. Crit Care. 2015;19:101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVincent JL, Nielsen ND, Shapiro NI, Gerbasi ME, Grossman A, Doroff R, et al. Mean arterial pressure and mortality in patients with distributive shock: a retrospective analysis of the MIMIC-III database. Ann Intensive Care. 2018;8:107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchneck E, Schulte D, Habig L, Ruhrmann S, Edinger F, Markmann M, et al. Hypotension Prediction Index based protocolized haemodynamic management reduces the incidence and duration of intraoperative hypotension in primary total hip arthroplasty: a single centre feasibility randomised blinded prospective interventional trial. J Clin Monit Comput. 2020;34:1149\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurabito P, Astuto M, Sanfilippo F, La Via L, Vasile F, Basile F et al. Proactive Management of Intraoperative Hypotension Reduces Biomarkers of Organ Injury and Oxidative Stress during Elective Non-Cardiac Surgery: A Pilot Randomized Controlled Trial. J Clin Med. 2022;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŠribar A, Jurinjak IS, Almahariq H, Bandić I, Matošević J, Pejić J, Peršec J. Hypotension prediction index guided versus conventional goal directed therapy to reduce intraoperative hypotension during thoracic surgery: a randomized trial. BMC Anesthesiol. 2023;23:101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaheshwari K, Shimada T, Yang D, Khanna S, Cywinski JB, Irefin SA, et al. Hypotension Prediction Index for Prevention of Hypotension during Moderate- to High-risk Noncardiac Surgery. Anesthesiology. 2020;133:1214\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, et al. Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis. Anesthesiology. 2018;129:663\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWijnberge M, van der Ster BJP, Geerts BF, de Beer F, Beurskens C, Emal D, et al. Clinical performance of a machine-learning algorithm to predict intra-operative hypotension with noninvasive arterial pressure waveforms: A cohort study. Eur J Anaesthesiol. 2021;38:609\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Ven WH, Terwindt LE, Risvanoglu N, Ie ELK, Wijnberge M, Veelo DP et al. Performance of a machine-learning algorithm to predict hypotension in mechanically ventilated patients with COVID-19 admitted to the intensive care unit: a cohort study. J Clin Monit Comput. 2021:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsoumpa M, Kyttari A, Matiatou S, Tzoufi M, Griva P, Pikoulis E et al. The Use of the Hypotension Prediction Index Integrated in an Algorithm of Goal Directed Hemodynamic Treatment during Moderate and High-Risk Surgery. J Clin Med. 2021;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, Berge P, et al. Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA. 2020;323:1052\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrassanito L, Giuri PP, Vassalli F, Piersanti A, Garcia MIM, Sonnino C, et al. Hypotension Prediction Index guided Goal Directed therapy and the amount of Hypotension during Major Gynaecologic Oncologic Surgery: a Randomized Controlled clinical Trial. J Clin Monit Comput. 2023;37:1081\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M, Ferrer R, et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. Crit Care Med. 2017;45:486\u0026ndash;552.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSessler DI, Bloomstone JA, Aronson S, Berry C, Gan TJ, Kellum JA, et al. Perioperative Quality Initiative consensus statement on intraoperative blood pressure, risk and outcomes for elective surgery. Br J Anaesth. 2019;122:563\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKleinman B, Powell S, Kumar P, Gardner Reed M. The Fast Flush Test Measures the Dynamic Response of the Entire Blood Pressure Monitoring System. Anesthesiology. 1992;77:1215\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaheshwari K, Khanna S, Bajracharya GR, Makarova N, Riter Q, Raza S, et al. A Randomized Trial of Continuous Noninvasive Blood Pressure Monitoring During Noncardiac Surgery. Anesth Analg. 2018;127:424\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYouden WJ. Index for rating diagnostic tests. Cancer. 1950;3:32\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;nez P\u0026eacute;rez JA. P\u0026eacute;rez Martin PS. [ROC curve]. Semergen. 2023;49:101821.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39:561\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzenne B, Subtil F, Maucort-Boulch D. The precision\u0026ndash;recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases. J Clin Epidemiol. 2015;68:855\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDW Hosmer SL. Applied Logistic Regression. 2nd Ed ed. New York, NY John Wiley and Sons; 2000.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Backer D, Cecconi M, Lipman J, Machado F, Myatra SN, Ostermann M, et al. Challenges in the management of septic shock: a narrative review. Intensive Care Med. 2019;45:420\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Ven WH, Veelo DP, Wijnberge M, van der Ster BJP, Vlaar APJ, Geerts BF. One of the first validations of an artificial intelligence algorithm for clinical use: The impact on intraoperative hypotension prediction and clinical decision-making. Surgery. 2021;169:1300\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnevoldsen J, Vistisen ST. Performance of the Hypotension Prediction Index May Be Overestimated Due to Selection Bias. Anesthesiology. 2022;137:283\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies SJ, Vistisen ST, Jian Z, Hatib F, Scheeren TWL. Ability of an Arterial Waveform Analysis-Derived Hypotension Prediction Index to Predict Future Hypotensive Events in Surgical Patients. Anesth Analg. 2020;130:352\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaheshwari K, Buddi S, Jian Z, Settels J, Shimada T, Cohen B, et al. Performance of the Hypotension Prediction Index with non-invasive arterial pressure waveforms in non-cardiac surgical patients. J Clin Monit Comput. 2021;35:71\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies SJ, Sessler DI, Jian Z, Fleming NW, Mythen M, Maheshwari K, Veelo DP, Vlaar AP, Settels J, Scheeren T, vd Ster BJP, Sander M, Cannesson M. F.Hatib, Comparison of differences in cohort (forwards) and case control (backwards) methodological approaches for validation of the hypotension prediction index, Anesthesiology. 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Area under the threshold, arterial waveform, blood pressure, hemodynamic monitoring, machine learning, artificial intelligence","lastPublishedDoi":"10.21203/rs.3.rs-4169157/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4169157/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eHypotension is associated with adverse outcomes in patients admitted to the intensive care unit (ICU). The application of an arterial blood pressure derived algorithm predicting hypotension significantly reduced hypotension during surgery. This Hypotension Prediction Index (HPI), calculates the likelihood (range 0-100) of hypotension occurring within minutes. In this study, the performance and clinical applicability of HPI is assessed in ICU patients.\u003c/p\u003e\u003ch2\u003eObjectives:\u003c/h2\u003e \u003cp\u003eThe primary objective was to assess overall performance of the HPI in ICU patients. Secondary objectives were to assess; the time to hypotensive events, change in the average sensitivity of HPI-85 preceding a hypotensive event, performance of HPI at clinical relevant threshold (HPI\u0026thinsp;\u0026ge;\u0026thinsp;85), and differences in patient subgroups.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe performed a prospective cohort study in an adult general ICU population of a tertiary academic medical centre using continuous arterial pressure waveform data. Hypotension was defined as mean arterial pressure below 65 mmHg for at least one minute. The predictive ability of HPI was evaluated using a forward analysis, calculating sensitivity, specificity, positive predictive value (PPV), time to event, receiver operating characteristic (ROC) curve and precision recall (PR) curve.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eIn 499 included and analysed patients, HPI showed an excellent predictive performance (area under ROC curve 0.97, PR curve 0.95), with a statistical optimum calculated at HPI 95 (Youden Index 0.87). Employing HPI\u0026thinsp;\u0026ge;\u0026thinsp;85 as an alarm resulted in a sensitivity of 99.7%, specificity of 76.3%, PPV of 83% and a median time to hypotensive event of 160 sec [IQR 60\u0026ndash;380]. There was no difference in HPI performance between different patient subgroups.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eHPI showed excellent performance in the prediction of hypotension in a general ICU population, without differences between subgroups. However, the average time between alarm (HPI\u0026thinsp;\u0026ge;\u0026thinsp;85) and the onset of hypotension is relatively short, which might affect the applicability and added value in an ICU setting.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eThis study was registered with the Netherlands Trial Register (NTR7349). The study was submitted and accepted for registration 2018-07-04, before the first patient was included. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.trialregister.nl/trial/7150\u003c/span\u003e\u003cspan address=\"https://www.trialregister.nl/trial/7150\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Source ID: W18_142#18.176\u003c/p\u003e","manuscriptTitle":"Validation and clinical applicability of the Hypotension Prediction Index in a general ICU population: a prospective observational cohort study Study acronym Prediction of Hemodynamic Instability in Patients Admitted to the ICU; the PHYSIC study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-04 17:24:53","doi":"10.21203/rs.3.rs-4169157/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb30bfe1-572b-4674-9fae-0df9536f71f7","owner":[],"postedDate":"April 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-01T17:44:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-04 17:24:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4169157","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4169157","identity":"rs-4169157","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}