Доклади на Българската академия на науките Comptes rendus de l’Acad´ emie bulgare des Sciences Tome 66, No 10, 2013 MATHEMATIQUES Informatiques
GENERALIZED NET MODEL OF A PROTOCOL FOR WEANING FROM MECHANICAL VENTILATION Lyudmila Todorova, Peter Vassilev, Mikhail Matveev, Vessela Krasteva, Irena Jekova, Stefan Hadjitodorov, Georgi Georgiev∗, Stoyan Milanov∗ (Submitted by Corresponding Member K. Atanassov on May 27, 2013) Abstract In the present work an attempt is made to evaluate objectively the ventilated patients’ condition from the monitored parameters (standard physiological parameters, parameters of the ventilation and respiratory mechanics, parameters of the gas exchange and energy expenditure) in order to determine their readiness for weaning from mechanical ventilation support. This research is a step in improving the care for ventilated patients in order to decrease the period of ventilation support. In the Central Intensive Care Unit, University Emergency Hospital “N. I. Pirogov” an investigation is conducted, with stages described in detail in the paper. A generalized model represents these phases. The decision criteria and the results of their application are evaluated by the so-constructed generalized net and a validity score is updated for each rule to be used in further considerations. The accuracy of the predictive rules has been estimated by calculating the sensitivity, specificity, the positive- and negative predictive value. Due to the relatively small number of patients, the developed GN-model will be refined in the course of the continuing investigation at the Central Intensive Care Unit, University Emergency Hospital “N. I. Pirogov”. Key words: generalized nets, weaning from mechanical ventilation, decision criteria
1. Introduction. The mechanical ventilation support is life-saving for the critically ill. The per cent of patients who undergo breathing support in intensive care units is significant – up to 90% of all entering patients. At the same time they account for 37% of the cost of the intensive care treatment. With the fading of the causes leading to ventilation support, it can be ended relatively This work is partially supported by the National Science Fund of Bulgaria under grants No DTK 02/48 – “System for Computer Aid of the Decisions for Weaning Critically Ill Ventilator Dependent from Mechanical Ventilation”.
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easy for the majority of the patients. For about 30% of the ventilated patients this is hard and long process and is known as “weaning from mechanical ventilation”. Every unnecessary delay in stopping the ventilator support carries a risk of complications (ventilator-associated pneumonia, incidents related to malfunction of the ventilator, etc.), increased mortality, prolonged stay of the patients in the intensive care units and increase of the costs for their treatment [1, 2 ]. On the other hand, the premature end of ventilation support, especially premature elimination of the artificial airways (extubation), is related with a specific set of life-threatening complications (direct complications of endotracheal intubation, aspiration of oropharyngeal/stomach content, infections, haemodynamic disturbances, etc.). Making an adequate, balanced decision for ending the ventilation support is difficult due to its dependencies on many complex objective and subjective factors, which requires interdisciplinary approach. In this direction are working clinicians, specialists in computer scientists, psychologists and engineers. One of the main venues in which the solution to this complex and multifactor problem is sought (weaning from mechanical ventilation) is standardization and algorithmization of the process of ending the ventilation support. Indirect evidence in support of such approach is presented by controlled clinical trials, which show the advantages of using a protocol for weaning from mechanical ventilation. 2. Materials and methods (Experimental). In the Central Intensive Care Unit, University Emergency Hospital “N. I. Pirogov” for 27 patients the readiness for weaning with state-of-the-art respiratory monitoring system – the Avea ventilator Avea (Viasys Healthcare Inc.), which uses activated integrated module for respiratory monitoring of Bicor Technologies has been tracked. The criteria under which the patients were included in the study are: • ventilator dependent patients – who have been on ventilator support for more than 24 h and/or patients, who have undergone an unsuccessful weaning attempt; • patients, for whom there is subjective doctor assessment for a minimal positive dynamic (lack of deterioration) with respect to the acute cause, leading to the ventilation support. All following criteria for exclusion from the experiment at any level are proposed (called stop criteria): X Possible respiratory or cardiopulmonary arrest; X SpO2 < 90% (PaO2 < 60 mmHg) FiO2 ≥ 0.5; X pH < 7.20 and/or mixed acidosis; X acute myocardial ischemia; X haemodynamic instability – circulation caused disturbances in consciousness level, hypotension, stagnation in lungs, acute rhythm-conductivity disturbance, sinuous tachycardia above 140/min, necessity for application of Dopamine/Dobutamine > 5 µg/kg/min or any dose of pressor; 1386
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X after careful assessment and reassessment: progressive narrowing of consciousness, pathological breathing pattern, clear subjective feeling of discomfort. The following algorithm for a spontaneous breathing trial is proposed: 1. Initial level – a patient covering all inclusion and exclusion criteria, calm and cooperative with acceptable minimal deviations in the level and content of consciousness. The esophageal balloon-catheter is positioned and its functionality is checked. Initial data of vital parameters, ventilator parameters and data from the additional metabolite monitoring module are collected. 2. Constant sedation is ensured with short-term hypnotic and a series of measurements, requiring sedated, but non-relaxed patients are done. 3. The patient is relaxed with short-term/intermediary myorelaxant to ensure conditions of passive respiratory system. The necessary maneuvers and measurements are done. 4. When clinical evidence of restoration of muscle power is present for a period of 5 min a mode of PSV with zero support from the respirator is set and the respective measurements are taken. This procedure is approved by the ethical commission at “N. I. Pirogov” and is done with constant supervision by the doctor, so it can be terminated at any time if there are signs that the patient is becoming unstable. This short period of additional mechanical effort is aimed at revealing eventual discrete circulatory and/or respiratory disturbances, which could lead to decompensation at a later stage in the spontaneous breathing trial. 5. Ventilation in mode PSV (eventually, PSV/CPAP) with pressure support, which initially ensures the achievement of physiological respiratory volumes and breathing patterns. This pressure support (PS) is gradually titrated in up to four identical steps until a level appropriate for a spontaneous breathing trial is reached. At each step a set of different parameters is measured. 6. Upon reaching the corresponding PS level measurements are done and the patient remains in the condition for spontaneous breathing trial. The outcome of the trial is measured at the second and at the twelfth hour. For the purposes of this investigation the result at the second hour is considered. Throughout the entire investigation the following parameters are being monitored: • a set of standard physiological parameters (ECG with heart rate – HR, rhythm and automatic analysis of the ST-segment; non-invasive oscillometric or invasive arterial – APM (mean arterial pressure), APS (systolic arterial pressure), APD (diastolic arterial pressure); pulsoxymetry – SpO2 (oxygen saturation), capnogram – RR (respiratory rate); • parameters of the ventilation and respiratory mechanics (volumetric parameters, airway pressures and esophageal pressure) – Tvexp (exhaled tidal volume), Tvinsp (inspired tidal volume), Mvexp(minute exhaled volume), Mvinsp (minute inspired volume), Tinsp (inspiratory time), Texp (exhalation time), VO2 Compt. rend. Acad. bulg. Sci., 66, No 10, 2013
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(total body oxygen uptake), VCO2 (carbon dioxide elimination); Ppeak (peak inspiratory pressure), Pplat(plateau pressure), Pmean (mean airway pressure), Raw (airways resistance); • parameters of the gas exchange and energy expenditure with the help of indirect calorimetry module – EE (energy expenditure), PETCO2 (partial pressure of end-tidal carbon dioxide), FiCO2 (fractional concentration of inspired CO2 ), RQ (respiratory quotient). 3. Generalized net model of the decision support algorithm for weaning. The Generalized nets (GN) were introduced by K. Atanassov as an extension of the Petri nets [3, 4 ]. Modelling by GNs permits simulation of the real process and the obtained results to be readily compared, averaged, analyzed and evaluated [5 ]. GNs allow the construction of detailed models, describing both the dynamics of the processes as well as the conditions imposed on them. Thus they are an adequate mathematical tool for illustration and tracking the considered processes. The GN model proposed here describes a weaning from the mechanical ventilation (MV) process and is characterized with static structure (Fig. 1), which reflects the different possible stages undergone by the patient. The dynamic structure of GN consists of tokens representing the monitored parameters (input and output for a given procedure) of a patient. The transition from one procedure to the next is done based on preliminary defined rules (stop criteria). Finally, a scoring of the proposed predictive rules is done (for the purpose of illustration we are giving two, using measurements at different phases). Once their validity is sufficiently high, they may be used in the place of the predicates for the following stages. The model consists of 7 transitions and 14 places. Place L1 represents the initial stage, L2 represents sedated relaxed state, L4 – represents sedated nonrelaxed patients, L6 – the zero level pressure support, L8 – the pressure support phase and L13 represents the spontaneous breathing trial. The patients, represented by α-tokens, enter the net at L1 . Here it is assumed that all entry criteria
Fig. 1. Generalized net representation of the proposed decision support
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are fulfilled. A β-token with characteristics “current rules scores” – a vector with dimension equal to the number of rules plus one and initial score of 1 awarded to all rules stays in L1 awaiting update of scores. The α-token has initial characteristics “list of measured parameters’ values; current rules scores” the latter identical to those of the β-token. The first transition has the form Z1 = h{L1 }, {L2 , L3 }, r1 i, r1 =
L2 L3 , L1 W1,2 W1,3
where W1,2 = “Stopping criterion has not been reached”, W1,3 = ¬W1,2 , where ¬P is the negation of the predicate P . In place L2 the α-token obtains a new characteristic “current measured values; rule 1 validity” – the latter is equal to “Pplat ≤ 17.65&AP M ≤ 104.97 ∨ HRmax ≥ 102&RQ > 0.675”. The second transition has the form Z2 = h{L2 }, {L4 , L5 }, r2 i, r2 = where
L4 L5 , L2 W2,4 W2,5
W2,4 = “Stopping criterion has not been reached”, W2,5 = ¬W2,4 .
A new characteristic “current measured values” is obtained here. The third transition has the form Z3 = h{L4 }, {L6 , L7 }, r3 i, r3 where
L6 L7 , L4 W4,5 W4,6
W4,5 = “Stopping criterion has not been reached”, W4,6 = ¬W4,5 .
In place L6 the α-token obtains a new characteristic “current measured values; rule 2 validity” – the latter is equal to “Tvexp > 201.95&((V O2 ≤ 351.65&ST > −0.8975) ∨ (V O2 > 351.65&RQ ≤ 0.6375))”. Compt. rend. Acad. bulg. Sci., 66, No 10, 2013
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After this procedure the patient is put on gradually diminishing pressure support level as represented by transition Z4 Z4 = h{L6 , L10 }, {L8 , L9 , L10 }, r4 i, r4 = L6 L10
L8 L9 L10 W6,8 W6,9 W6,10 , W10,8 W10,9 W10,10
where W6,8 = W10,8 = “Pressure support level for spontaneous breathing trial is reached”, W6,9 = W10,9 = “A stopping criterion is reached”, W6,10 = W10,10 = “Pressure support can be lowered & ¬W6,8 &¬W6,9 ”. The α-token obtains characteristic “current measured values”. The fifth transition has the form Z5 = h{L8 }, {L11 , L12 }, r5 i, r5 =
L11 L12 , L8 W8,11 W8,12
where W8,11 = “Stopping criterion has been reached”, W8,12 = ¬W8,11 . The characteristics are updated with the measured values in place L11 . The sixth transition represents the outcome of the breathing trial and has the form Z6 = h{L11 }, {L13 , L14 }, r6 i, r6 =
L11
L13 L14 , W11,13 W11,14
where W11,13 = “The patient breathed spontaneously for 2 h”, W11,14 = “The patient had to be returned to ventilation before the second hour”. In L13 , the α-token obtains new additional characteristic “outcome” equal to 1 and for each rule a new characteristic – “updated ith rule score = rule i validity & outcome.” In L14 , the α-token obtains new characteristic “outcome” equal to 0 and for each rule a new characteristic – “updated ith rule score = ¬ rule i validity & ¬ outcome.” The updated scores are passed to a β token which is then merged with the β token in L1 Z7 = h{L13 , L14 }, {L1 }, r7 i, 1390
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L1 r7 = L13 true . L14 true Our investigation shows that both rules considered here have a good agreement with the outcome result and may serve as successful predictors. Both were obtained after training over 22 patients (11 with successful outcome and 11 with unsuccessful) and verified on the remaining 5. 4. Results. The parameters that are statistically significant and have been included in the decision criteria are: Pplat, APM, HRmax, RQ, ST, Texp, Tinsp, Pmean, EE, TVexp, VO2 ; PETCO2 , Raw. For each procedure the most appropriate of them are summarized on Table 1. Table
1
Decision parameters by procedure Procedures Initial phase SNR SR PSV 0PS PSV (starting PS)
Included parameters Pplat, APM, HRmax, RQ ST , Texp, Tinsp, HRmax Pmean, APM, RQ, EE Tvexp, VO2; PETCO2, RQ Pplat, Raw, ST
On Table 2 the sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and negative predictive value (NPV) are calculated according to the formulas: SEN =
TP ; TP + FN
SPE =
TN ; TN + FP
PPV =
TP ; TP + FP
NPV =
TN , TN + FN
where TP is true positives; FP false positives; TN true negatives; FN false negatives. Table
2
The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and negative predictive value (NPV), calculated for all procedures preceding the spontaneous breathing trial Procedure Initial SNR SR PSV 0PS PSV (starting PS)
3
SEN% 100 100 84.62 100 92.31
Compt. rend. Acad. bulg. Sci., 66, No 10, 2013
SPE% 92.86 92.86 92.86 100 100
PPV% 92.86 92.86 91.67 100 100
NPV% 100 100 86.67 100 93.33
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5. Conclusions. Many intensive care specialists accept the idea that weaning from mechanical ventilation support would be aided by the application of adequate algorithms. The algorithms for automation of the weaning process from mechanical ventilation support (or some stages of it) reduce the period of mechanical ventilation compared to this of the patients weaned under the control of clinicians as simultaneously the participation of highly qualified medical personnel is not required. Equal attention to all the patients is guaranteed. The development of the protocol approach into automated decision support system for the clinical decisions is a logical step towards the objective decision making with less engagement of the medical personnel and eventual cost reduction of the treatment. The attempts for developing such systems in different centres are continuing as sometimes completely or partially close-loop modes of mechanical ventilation are integrated [6, 7 ]. In the present work a relatively simple decision criteria are proposed and good agreement with the spontaneous breathing trial is observed. One of the most informative steps in the procedure, however, requires the presence of medical doctor, namely the ventilation with zero pressure support, which makes difficult the large scale implementation of this approach in intensive care units. Due to the relatively small size of the currently considered patients the data collection in “N. I. Pirogov” continues with the purpose to improve the threshold values for the parameters in the decision criteria and to remove rules which fail to maintain good validity. REFERENCES [1 ] Frutos-Vivar F., N. D. Ferguson, A. Esteban. Intensive Care Med., 35, 2009, 775–778. [2 ] Principles and Practice of Mechanical Ventilation (ed. M. J. Tobin), New York, McGraw Hill, Inc, 2006. [3 ] Atanassov K. Generalized Nets, Singapore, New Jersey, London, World Scientific, 1991. [4 ] Atanassov K. On Generalized Nets Theory, Sofia, Prof. M. Drinov Publishing House, 2007. [5 ] Chakarov V. E., A. G. Shannon, J. G. Sorsich, K. T. Atanassov. Compt. rend. Acad. bulg. Sci., 61, 2008, No 6, 705–712. [6 ] Al´ıa I., A. Esteban. Critical Care, 4, 2000, No 2, 72–80. [7 ] Burns K. E. A., F. Lellouche, M. R. Lessard. Intensive Care Med., 34, 2008, No10, 1757–1765. Institute of Biophysics and Biomedical Engineering Bulgarian Academy of Sciences Acad. G. Bonchev Str., Bl. 105 1113 Sofia, Bulgaria e-mail:
[email protected] 1392
Central Intensive Care Unit University Emergency Hospital “Pirogov” 21, Totleben Blvd 1606 Sofia, Bulgaria e-mail:
[email protected] ∗
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