| Abstract|| |
Introduction: Trauma systems vary in performance during different time periods and may affect the patient outcomes, especially in resource-limited settings. The present study was undertaken to study the pattern, epidemiological profile, processes of care variations of trauma victims presenting during-hours and after-hours in a level 1 trauma Center of a lower middle-income country. Methodology: Retrospective analyses of prospectively collected data registry at a single tertiary care center. Data collected from 2013 to 2015 were analyzed. Patients with a history of trauma and admission to the center or death between arrival and admission were included. Isolated limb injury and patients dead on arrival were excluded. Results: Of 4692, 1789 (38.1%) patients arrived and were admitted during-hours and 2903 (61.9%) after-hours. The overall in-hospital mortality was 14.9% in the cohort. Moreover, it was 16.10% during after-hours in comparison to 13.0% during-hours. The Revised Trauma Score was statistically different during-hours and after-hours suggesting patients with greater physiological derangement after-hours. The Kaplan–Meier survival curves for 7 days were comparable in two groups with the log-rank test of 078. The proportion of initial radiological investigations (chest X-ray, focused assessment sonography in trauma [FAST], and computerized tomography [CT] scans) was ranged from 84.9% for CT scans in the cohort to 99.3% for FAST. Conclusions: Processes of care do not differ significantly for the patients admitted at a level 1 trauma center irrespective of time of the day. Although survival probability for the initial 7 days of follow-up is comparable between two groups; however, for 30 and 90 days of follow-up they are significantly different between during-hours and after-hours, likely due to injury severity.
Keywords: After-hours, during-hours, processes of care, survival probabilities, trauma outcomes
|How to cite this article:|
Soni KD, Mahindrakar S, Kaushik G, Kumar S, Sagar S, Gupta A. Do the care process and survival chances differ in patients arriving to a level 1 Indian trauma center, during-hours and after-hours?. J Emerg Trauma Shock 2019;12:128-34
|How to cite this URL:|
Soni KD, Mahindrakar S, Kaushik G, Kumar S, Sagar S, Gupta A. Do the care process and survival chances differ in patients arriving to a level 1 Indian trauma center, during-hours and after-hours?. J Emerg Trauma Shock [serial online] 2019 [cited 2019 Dec 8];12:128-34. Available from: http://www.onlinejets.org/text.asp?2019/12/2/128/259200
| Introduction|| |
Road traffic injuries (RTIs) cause 1.35 million each year, the majority of victims being 15–29 years old. Half of them are vulnerable road users including pedestrians, cyclists, and motorcyclists. The majority of deaths occur in low- and middle-income countries where patients are managed in insufficient and overloaded health systems. Hospitals strive hard to maintain consistency in acute care service delivery and limiting mortality and morbidity, It is assumed that patients receive similar levels of care irrespective of time of presentation, and the overall outcome does not vary based on the day of presentation and timing of arrival.
Variation in outcomes based on the timing of patient presentation has been reported across medicine.,,, It has also been reported for weekends and holidays.,,, Not only the time of arrival but also the time of discharges is implicated for deviation in outcomes. Multiple reasons have been cited previously for observed variations including staffing pattern disparity, human factors, fatigue during after-hours, lack of sleep affecting cognitive thinking, and slackening of hospital care processes.,, These factors could affect the management of the patients receiving acute care and can have a huge impact on overall prognosis.
Trauma systems also exhibit discrepancies in the performance based on the timing of arrival to the hospital.,, Processes of care may show fluctuations across different time periods. However, there is considerable inconsistency in the literature. Some centers report no difference in outcomes, while some to demonstrate a significant effect.,,,, Therefore, the present study was undertaken to compare the epidemiological profile, processes of care, and outcomes in patients presenting during- and after-hours to a level 1 urban trauma center in a lower middle-income country.
| Methodology|| |
We conducted a retrospective analysis of single-center data collected as a part of the prospective cohort study Towards Improved Trauma Care Outcomes (TITCO) in India.
A data collector collected the observed patient's prospective data on regular rotation (day and night) basis from the emergency room (ER). Data of the patients who arrived during nonobserved hours were retrospectively collected from the Central Patient Registry System - Computerized Patient Record System. The data collector had as a minimum a master's qualification and they were trained and supervised by the project manager. The staffing in the Emergency Department during-hours consisted of Faculties from emergency medicine (MD-01), neurosurgery (Mch-01), orthopedics (MS-01), and faculty form trauma surgery (MS-01). However, senior residents (emergency medicine - 02, trauma surgery - 02, orthopedics - 01, neurosurgery - 01, ENT - 01, and pediatric surgery - 01), junior residents (emergency medicine - 03 and trauma surgery - 02), and nursing officers (Red Area - 05, Yellow Area - 05, Green Area - 01, Minor OT - 01, Counter - 01, and Triage officer - 01) were also posted in ED. The same staffing pattern was followed after-hours.
Data were uploaded on a weekly basis followed by data quality meetings. Data collected from 2013 to 2015 for Jai Prakash Narayan Apex Trauma Center, All India Institute of Medical Sciences, New Delhi, India, a dedicated level 1 trauma Center was analyzed.
Patients with the history of injury with either falls, railway and road accidents, assaults or other trauma with admission, or death between arrival and admission, were included for the current study. Isolated limb injury and patients dead on arrival were excluded.
Since our objective was to compare the epidemiological profile, processes of care, and outcomes in patients presenting during- and after-hours to a level 1 urban trauma center in a lower middle-income country, we selected variables representing the above domain and available in the database.
Categorical variables were described in frequency and percentages. These included sex, mechanism of injury, mode of transport, shock on arrival systolic blood pressure (SBP) <90 mmHg, Glasgow Coma Score (GCS), and the type of injury. Descriptive data on arrival and within 24 h included were SBP, GCS, intubation within 1 h and between 1 and 24 h, surgical airway within 1 h and between 1 and 24 h, and operation theater procedure within 1 h and between 1 and 24 h. Continuous variables were described in mean and standard deviation. These include age in years, heart rate on presentation, SBP on presentation, GCS, and oxygen saturation. The processes of care were represented by the percentage of the chest X-ray, focused assessment with sonography for trauma (FAST), and computerized tomography (CT) scan performed within an hour of arrival. Severity Score (Injury Severity Score 2, New ISS (NISS), and Revised Trauma Score [RTS]) were also calculated based on datasets available in the registry.
We considered all-cause in-hospital mortality as our primary outcome, length of intensive care unit (ICU) stay and length of mechanical ventilation as secondary outcomes.
The two groups were created based on the timing of arrival. During-hours was defined as between 08.00 am and 18.00 pm. After-hours was defined as between 18:01 pm and 07.59 pm. This grouping was chosen because the maximum staff strength including senior consultants were physically present during-hours and beyond 18.00 pm the staff strength reduces, and the center was mostly manned by junior consultants.
Categorical variables were described in percentages and compared between two groups using Chi-square method, while continuous variables were described in mean and standard deviation and compared between two groups using Student's t-test. P < 0.05 was considered statistically significant. A stratified analysis based on the severity of illness ISS ≤15 and ISS >15 was carried out to remove the confounding effect of severity of illness.
A survival analysis based on Kaplan–Meier curve predicting for 7, 30, and 90 days of hospital outcome was also constructed to look at survival probabilities in two groups. A log-rank test was used to test the variability of care. All analyses were done in IBM SPSS Statistics for Windows, version 23 (IBM Corp., Armonk, N.Y., USA).
Ethical clearance was obtained from each of the participating hospitals for TITCO including the Center for the current study, and it included a waiver of informed consent. The name of the ethical body and the reference number were Institute Ethics Committee, All India Institute of Medical Sciences (EC/NP-279/2013 RP-Ol/2013).
| Results|| |
Of 4692 patients, 2903 (61.9%) came after-hours, and 1789 (38.1%) patients arrived during-hours. Males were in the majority in both groups, and most patients were younger (Mean age: 32 years). RTI was the most common mechanism of the injury. It was higher in after-hours (56%) compared to during-hours (51.3%). The ambulance was the most preferred mode of transport for patients in both groups. Penetrating injuries were higher (9.7%) in patients arriving after-hours. More patients with GCS <9 arrived after-hours (21.8%) compared to during-hours (18.8%). A higher percentage of patients was in shock physiology (SBP ≤90) after-hours (11.47%) compared to during-hours (09.0%) [Table 1].
|Table 1: Baseline characteristics of patients arriving after-hours and during-hours in a level 1 Trauma Center of the University Hospitala|
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Although the heart rate and Glasgow coma scale showed statistically significant differences between the groups arriving after-hours and during-hours, the clinical difference was insignificant and could be considered comparable in both groups [Table 2].
|Table 2: Distribution of age characteristic, hemodynamic parameters, oxygen saturation status, and Glasgow Coma Score based on timing of arrival of patients in level 1 Trauma Center of an Urban University Hospital|
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NISS and ISS were higher among the group of patients arriving after-hours, but RTS was significantly lower (P< 0.05) among the after-hours patients' group in comparison to during-hours patients group [Table 3].
|Table 3: Trauma severity scores distribution among total numbers of patients, patients arriving after-hours, and patients arriving during-hours|
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The proportion of initial radiological investigations (Chest X-ray, FAST, and CT scans) was ranged from 84.9% for CT scans to 99.3% for FAST for the whole cohort. Similar numbers were noted for the above modalities during-hours and after-hours group [Table 4].
|Table 4: Distribution of diagnostic procedures (performed within 1 h of arrival) among patients arriving after-hours and during-hour|
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There were 699 deaths (14.90%) in the complete cohort. In-hospital mortality in patients arriving after-hours was significantly higher (16.10%) compared to patients arriving during-hours (13.00%); P < 0.003 [Table 5]. Even within the subgroups (based on ISSs), the in-hospital mortality remained significantly higher in patients arriving after-hours. The length of mechanical ventilation and length of ICU stay was comparable in both groups [Table 5].
|Table 5: Comparing clinical outcomes (In hospital mortality, length of mechanical ventilation, ICU Stay) among group of patients arriving after-hours and during-hours|
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The Kaplan–Meier survival curves for 7 days were comparable in two groups with the log-rank test of 0.078. However, at 30 days and 90 days of follow-up, the survival probabilities were significantly different within two groups (log-rank test, 0.014 and 0.008, respectively) [Figure 1] and [Table 6].
|Figure 1: Kaplan–Meir curves predicting the survival with hospital stay of 7 days, 30 days, and 90 days|
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After comparing the outcomes on the basis of the weekend, we found that 489 (14.5%) patients out of 3352 died during weekdays, and 210 (15.6%) patients out of 1340 died during the weekend. However, the difference was not statistically significant (P = 0.364) [Table 7].
|Table 7: Comparison of outcome (in-hospital mortality) on the basis of admission during weekdays and weekends|
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| Discussion|| |
The present study compared the epidemiological profile, processes of care, and outcomes in patients presenting during- and after-hours to a level 1 urban trauma center in a lower middle-income country. Results demonstrate a significant advantage to the patient presenting during-hours. The mortality of patients is lower (13%) in comparison to after-hours (16.10%). The difference is statistically significant (P = 0.003). The difference becomes more apparent and remains consistent, even after stratification based on the severity of disease, i.e., ISS ≤15 (42.86% vs. 57.14%) and ISS >15 (32.82% vs. 67.18%). The survival probability for 90 days also demonstrates significant differences between the two cohorts of patients with higher survival for the during-hours cohort. The length of ICU stay and length of mechanical ventilation was analogous between the groups.
The results are intriguing since as they clearly mean the overall in-hospital mortality is affected by the presentation time of the patient. The severity of illness – based on ISS – also does not explain the difference between the groups. Stratified analysis rules out the confounding effect of the ISS.
To explain the difference, we looked at our processes of care indicators and theorized that the difference between the two groups might have arisen due to the difference in the processes of care during-hours and after-hours. However, we could not identify the major disparities. Most of the processes of care indicators – intubation within 1 h, surgical airway within 1 h, and operation room procedures within 1 h frequencies were comparable. The numbers of radiological investigations – chest X-ray, CT scan, and FAST were also proportionate in both groups.
Although not statistically significant, there was a slightly greater number of patients with GCS ≤8 in the after-hours cohort (21.8% vs. 18.8%). While the proportion of patient arriving with SBP <90 was also higher in after-hours group (11.47% vs. 8.95%) and this was statistically significant (P< 0.05). The RTS score was significantly lower in patients presenting after-hours (P< 0.05), and this may have skewed the mortality peak toward the after-hours group. It has been previously shown to be a better predictor in comparison to ISS in the same database and thereby more accurately reflects the severity of patients than ISS. This may explain the higher proportion of deaths in the after-hours due to increased severity of illness based on RTS though the effect was not visible on stratified analysis based on ISS.
The higher percentage of penetrating trauma in the after-hours group may explicate the finding of the study since penetrating trauma has been previously considered as an independent risk factor for poor outcome. Furthermore, an increased number of patients underwent emergency surgery (31.8% vs. 28.1%) after-hours in comparison to during-hours, which was significant statistically (P< 0.001). This might have contributed to increased mortality as emergency surgical procedures could increase the perioperative complications, while in on hours group, the more number of patients was treated nonoperatively.,
Prior studies that address the above question have shown conflicting results. In a similar study from a German level 1 trauma center, investigators compared the quality of trauma care and outcomes in 394 multiply injured patients with ISS >16, during business hours and non business hours. They found no difference in 24-h mortality and inhospitable mortality. However, the study had a small sample size in comparison to ours (394 vs. 4691), the definition of during-hours and after-hours was also different, they defined business hours (during-hours) from 8 am to 4 pm, while we defined during-hours from 8 am to 6 pm, and after-hours from 6 to 8 am morning (nonbusiness hours, 4–8 am). It may also be due to different trauma systems which exist between the developed systems of care and developing nations. They have a robust prehospital trauma care which provides timely intervention and also optimizes the physiology before arrival to the hospital, whereas in the present study setting, prehospital trauma care is arguably nonexistent and patients often arrive in a worse physiological state. The process of care was similar during-hours and after-hours in both the studies reflecting comparable in-hospital settings.
Ono et al. reported longer ER stay for severely injured patients and increased the risk of adverse events in the ER after-hours, but this did not translate into mortality difference. The lack of effect on mortality may be attributed to the exclusion of early deaths ( first peak) in contrast to the present study, which includes all deaths after arrival to the ER. Mazahir et al. from Australia, showed increased rates of missed injury in patients presenting after-hours following a trauma. Our study did not look at the rate of missed injury. Another study reported poor outcomes in the high-risk subgroup of patients presenting with acute traumatic coagulopathy during the after-hours period. They reported increased odd's ratio of mortality. The results were in concurrence with the current study; however, we had a much broader spectrum of trauma patients and a larger sample size. Whether such an effect exists in our group of traumatic coagulopathy remains to be identifiable.
The after-hours effect is not only demonstrated in trauma settings but also shown for other conditions. The after-hours presentation is a risk factor for patients with acute conditions such as stroke, traumatic brain injury, pulmonary embolism, myocardial infarction, cardiac arrest, and ruptured an aortic aneurysm.,,,,, The factors speculated to increase the mortality in our study may be: reduction in staffing levels during off hours, impairment in performance in emergency settings due to fatigue,, disrupted circadian rhythms, and increased medical errors and complications., More research is needed to determine the contribution of each on mortality.
The major strength of the present study is the large sample size and the long duration of the study. This limits the bias introduced due to small sample size and time period; which could affect the validity as it's been known that the process of care varies between different time points and small sample underpowers the findings. A broad inclusion criterion also helps in recognizing effect in nutshell and excludes effects confined only to specific diseases. The present study is limited by the retrospective nature, a secondary analysis of single-center data – albeit part of a multicenter study (TITCO). Whether the findings are generalizable to other centers are largely unknown. The present study included during-hours and after-hours on weekdays, weekends, and holidays. Whether, this after-hours effect changes during weekends remains unknown as no further subgroups were made. Prehospital care, the time difference between the time of injury and first contact with the care provider may be potential confounders of this study.
| Conclusions|| |
The above study suggests that the processes of care do not differ significantly for the patients arriving at a level 1 trauma center irrespective of time of the day. Although survival probability for initial 7 days of follow-up is comparable between two groups, for 30 days and 90 days of follow-up they are significantly different between during-hours and after-hours, and this might be attributable to greater physiological derangements in patients presenting after-hours based on RTS score.
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Conflicts of interest
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Dr. Amit Gupta
Department of Trauma Surgery and Critical Care, Jai Prakash Narayan Trauma Centre, AIIMS, New Delhi
Source of Support: None, Conflict of Interest: None
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]