PROPUESTAS DE FUTURO

This section describes the phase one study for this research. This study was planned according to lessons learnt from the pilot study. Guided by the pilot study, the data

collection tool was refined (Appendix II). Furthermore, the pilot study helped to refine the research objectives for the phase one study; these are described in the next section.

4.6.1 Aim  

The overall aim of this study was to examine the documentation of vital signs in the EHR of patients who subsequently had a cardiac arrest. The objectives were:

• to identify the extent to which vital signs were recorded in the EHR in the final 24 hours prior to cardiac arrest in an acute hospital,

• to establish the location of the vital sign recordings within the EHR, and how they were documented, and

• to examine whether documented vital signs could reveal information about a patient's risk of deterioration by aligning these to two track and trigger systems (BAS and ViEWS).

4.6.2 Method  

The method regarding hospital setting and data sources are described earlier in this chapter (4.3.2-4.3.4) so the description of method for the principle study begins with the data collection.

4.6.2.1   Data  collection  

As mentioned, the study hospital joined the SRICA in January 2007. The total number of patients in the register for the study hospital was 310, but 62 of those did not have an EHR as the register was introduced before the EHR was implemented in June 2007. The patients in the register from 1 January to 31 May 2007 were therefore excluded. This left 248

patients that were in the register who had an EHR.

The data collection began on 29 September 2011 and ended on 15 November 2011. Paper copies of the data collection tool were used and all the required data were collected from

the register and the EHR according to the pre-designed tool. Twenty of these 248 cases were excluded from the study for various reasons. For example, it was observed during data collection from the register that some patients had a series of cardiac arrests within a relatively short period and each of these were registered in the register. In these cases, only the data related to the first cardiac arrest were collected from the EHR. The rationale for this decision was that the data collected would record the vital signs that were taken before the first cardiac arrest. In two cases, patients were known by the researcher, so these were set aside immediately after being recognised to avoid any breaches of confidentiality. The final number of patients from whom data were collected was 228. Thus, the main phase of the study included 228 patients who suffered a cardiac arrest in the study hospital between 2007 and 2011 and who had an EHR for their period of care.

It was noted during data collection that some patients did not have any vital signs recorded in the EHR prior to the cardiac arrest. There were three possible reasons for this. First, it could be that a patient had a cardiac arrest on admission to the ED, before anyone had time to take any vital sign recordings. Second, patients who were diagnosed with an acute myocardial infarction before admission (for example, in the ambulance via mobile ECG readings) were taken directly to the cardiac catheterisation laboratory for immediate angiography and treatment. This involves the insertion of a catheter into the heart via an artery. The nature of this procedure may over-stimulate the cardiac muscle which may then cause a life-threatening cardiac rhythm such as ventricular fibrillation. Ventricular

fibrillation is a cardiac rhythm, with loss of cardiac output, and is one type of cardiac arrest. It requires immediate cardio-pulmonary resuscitation (CPR). Thus, if this happened during cardiac intervention, it was registered as a cardiac arrest. A third possibility was that

patients who were transferred directly to the cardiac catheterisation laboratory were critically ill and may have had a cardiac arrest in the laboratory because of their critical cardiac condition. Therefore, when conducting the data collection, additional information was added in the note section of the data collection tool to clarify the reason why patients belonging to the above categories did not have any vital signs recorded.

From the register, for each case the required information was collected according to the data collection tool. Next, the patient's unique ten-figure identification (ID) number was used to access each patient's EHR. These ID figures were jotted down in pencil and erased after each EHR had been accessed, to ensure patient confidentiality. Patient names were not used at any point in the data collection.

The next stage was to collect all of the patient's vital signs. These could be found in three separate sections: the template, the journal and the report sheet (described in more detail in Section 4.5.1 of this chapter). Here, there were the headings: temperature, pulse, respiratory rate, blood pressure and oxygen saturation. The information was transferred to the data collection tool systematically.

The next section to be examined was the 'journal', selected by clicking on 'journal' in the toolbar at the top of the page. The 'journal' section is where all members of the health care team can enter data on the patient. The last section was the report sheet, which was also selected from the left hand margin of the open journal page. Data from each of the vital signs recorded were collected in the same manner from the three sections of the EHR. Entries were also made in the data collection tool to show in which section of the EHR vital signs were documented. It was noticed that all vital signs were documented in numerical form and no visual graph was available.

4.6.2.2   Observations  during  data  collection  with  implications  for  SPSS  

Several observations during the data collection were important for refining the variables for data analysis and as a result of these observations several changes were made to the

variables entered into SPSS. For example, in cases where a patient's cardiac activity was being continuously monitored by telemetry, the heart rate varies by the second. Thus, in the EHR it was sometimes documented as being between two numbers, for example, 80- 120. In these cases the mean of the two values was calculated to obtain one value for the data collection. A new variable was added to show if the respiratory rate had been recorded in the Emergency Department (ED), as it was observed during the data collection that respiratory rate was often recorded while patients were in the ED. In the pilot study, only two variables regarding levels of care were specified and included in the SPSS programme: intensive care or general ward care. However, during data collection for this study, it was found necessary to be more specific about the exact location of patients when they had a cardiac arrest. Thus, five new categories were added to the variables in SPSS: Emergency Department (ED), general ward, catheterisation laboratory, cardiac intensive care unit (CICU) and intensive care unit (ICU).

4.6.2.3   Signs  of  deterioration    

Another important aspect of preparing for analysis was in relation to detecting

deteriorating patients. To enable this, it was decided to calculate the values of vital signs according to two track-and-trigger systems, to investigate whether or not patients exhibited

signs of deterioration. The first TTS, BAS/90-30-90, was selected as it was currently used in the study hospital (table 4.5).

Table 4.5 BAS 90-30-90 Blodtryck, Andning, Saturation (BAS)

Vital sign Threshold value

B Systolic blood pressure (blodtryck) <90 A Respiratory rate (andning) >30 S Oxygen saturation % (saturation) <90

The second system was the aggregated weighted scoring system called ViEWS (Prytherch et al., 2010) (shown in table 4.6). In this system, there are seven vital parameters with each vital parameter being given a score between zero and three depending on a graded scoring system. As a result of the pilot study, ViEWS had been selected as the 'gold standard' model on which to base the choice of vital signs to be collected. However, ViEWS was not used at the study hospital, therefore the variables used in this system would not have been required to be routinely recorded. Nevertheless, it was decided to allocate ViEWS values to each set of vital signs so that a calculation could be made on the vital signs which were available. In this way, risk of patient deterioration could possibly be detected. (See Chapter 2, section 2.5.2).

Table 4.6 ViEWS (Prytherch et al., 2010) Score 3 2 1 0 1 2 3 Pulse (bpm) ≤ 40 41-50 51-90 91-100 111- 130 ≥131 Breathing (rpm) ≤ 8 9-11 12-20 21-24 ≥ 25 Temperature (C°) ≤ 35.0 35.1- 36.0 36.1- 38.0 38.1- 39.0 ≥ 39.1 Systolic BP (mmHg) ≤ 90 90-100 101- 110 111- 249 ≥ 250 SaO₂ (%) ≤ 91 92-93 94-95 ≥ 96

Inspired O2 Air Any O2

CNS (use AVPU scale)

Alert Voice

Pain

Unresponsive Note: ViEWS = early warning score for use in VitalPACTM _{system (The Learning Clinic}

Ltd, 2012); bpm= beats per minute; rpm= respirations per minute; Systolic BP= systolic blood pressure; SaO2= saturation of oxygen; CNS= central nervous system; AVPU= alert,

responds to voice, responds to pain, unresponsive 4.6.2.4   Data  analysis  

Once the data had been entered manually, they were entered into SPSS. SPSS 19 was used to carry out the analyses and describe the results. Although a wide range of data had been collected, the information that was analysed in SPSS pertained to descriptive statistics of demographic variables, the hospital departments and details about vital signs. Univariate descriptive analysis, bivariate analysis and logistic regression analyses of the data were also performed. Logistic regression was employed to identify any association between a number of independent variables, (for example, vital signs and hospital departments) and survival to resuscitation and survival to discharge, the dependent variables.

Reliability and validity of the quantitative data

As noted in Chapter 3, the quality of quantitative data is assessed in terms of reliability and validity. Reliability and validity of the quantitative data was established by carefully

considering the research questions and the design of the data collection tool. A discussion about reliability and validity of the quantitative data is provided in Section 6.4.1.