Evaluación de la actividad bactericida de amoxicilina y

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All statistical analyses were performed using SAS 9.4^© statistical software (Cary, NC).

34 3. RESULTS

3.1 Study Population

Figure 5 summarizes the flow chart the cohort groups.

The study population was comprised of 810,279 patients who had a major surgical procedure from January 2004 through December 2014. Table 5 summarizes the patient demographics in the whole cohort and by study groups. The most frequent surgery type was orthopedic that represented 33.2% of the whole cohort followed by gynecology (22.6%). Most surgeries occurred in the urban setting (84.9%), in non-academic institutions (68.7%) and were elective procedures (83.3%).

The pre-implementation CCRT group was comprised of 106,744 patients while the post-implementation CCRT group was comprised of 319,080. The pre-post-implementation non-CCRT group was comprised of 89,164 patients while the post-implementation non-CCRT group was comprised of 295,291 patients. In this univariate analysis, the main differences between groups were in regard to type of institution and bed size. Most patients treated in the CCRT groups were

Total n=810,279

Patients with a complication n=148,882 (18.4%)

FTR n=13,659 (9.2%) No FTR n=135,223 (90.8%)

35

treated more frequently in academic institutions and centers with a larger bed size. Patients age, sex, income quintile and cancer diagnosis were similar across all study groups. Patients in the CCRT group had more comorbidities when calculating both the ADG [CCRT group pre 10+

(52%) vs. non-CCRT pre (47.1%) and Elixhauser scores [CCRT group pre Elixhauser ≥5 (12.8%) vs. non-CCRT pre (10%)]. Finally, more vascular and thoracic procedures were performed in centers that implemented CCRTs when compared to those that did not.

36 Table 5: Demographics in the Study Population

VARIABLE OVERALL

37 3.1.1 Outcomes in the Study Group

The outcome measures of patients in the study group are summarized in Table 6. Death within 30 days occurred in 31,659/810,279 (1.7%) of the patients. Among all patients, 148,882 (18.4%) developed a postoperative complication. The proportion of patients requiring transfer to ICU was 3.8% and the proportion of patients requiring an unplanned return to the operating room was 7.9%.

The proportion of death within 30 days was similar across all study groups and was ~2%.

Similarly, the proportion of patients developing postoperative complications was similar across the four study groups. In general, no major differences were observed between the different study groups in the outcome measures.

Table 6: Outcomes of Study Population

OUTCOME

Rate of Transfer to ICU

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3.2 Population of patients developing a postoperative complication

In order to analyze the main study outcome, FTR, a separate of group of patients that underwent a major surgical procedure and developed a postoperative complication was analyzed.

The patient’s demographics in this group is summarized in Table 7.

A total of 148,882 patients developed a postoperative complication after a major surgical procedure. This is the population where the primary study outcome (FTR) was investigated.

Most patients were over the age of 60 and were patients with comorbidities as assessed both by the ADG and Elixhauser scores. Over 50% of patients developing a postoperative complication had an intervention categorized as general surgery and most patients had surgery in a non-academic small (<100 beds) institution.

The age and sex distribution of the patients was similar across study groups. However, patients in the non-CCRT groups had fewer comorbidities and as previously observed with the whole cohort, more frequently were treated in a non-academic institution.

39

Table 7: Demographics of patients who developed a postoperative complication

VARIABLE OVERALL

40

3.2.1 Outcomes in the population of patients developing a postoperative complication Primary Outcome

Figure 6 demonstrates the proportion of patients with a postoperative complication that died within 30 days (FTR) before and after the implementation of CCRTs both in the CCRT group and in the non-CCRT group. As depicted in Table 8, the overall proportion of FTR was 9.2%. Among patients in the CCRT group there was a 2.4% decrease in FTR before and after the implementation of these programs. In comparison, in the same study period, centers that did not implement CCRTs had a 1.9% decrease in the proportion of patients dying after a complication.

Figure 6 Proportion of patients developing failure to rescue among the CCRT and non-CCRT groups before and after the implementation of CCRT programs

Table 8 summarizes other outcome measures in this patient population. Most patients developed a single postoperative complication. In this univariate analysis, the rate of unplanned

41

return to the operating room was 43%. In the CCRT group there was a 3.1% increase in this rate before and after the implementation CCRTs whilst in the non-CCRT group this increase was 2.4%. There was also a decrease in the proportion of patients requiring a transfer to the ICU before and after the implementation, both in the CCRT groups (3.7%) and the non-CCRT groups (3.6%). An increase in the rate of cardiopulmonary arrests was also found in both the CCRT (4.4%) and non-CCRT (3.2%) groups after the implementation of CCRTs.

Table 8: Outcomes of Patients developing a postoperative complication

OUTCOME

3.3 Risk Factors for Failure to Rescue

Among the population of patients who developed a surgical complication (n=144,882) we sought to determine the risk factors for FTR. Table 9 summarizes the results of the multivariable logistic regression. As expected with older age, there was an increase in the relative risk of FTR that was found to be up to 4.04 (3.73-4.37) in those patients >80 years old compared to patients between 18 and 60 years old. Male patients were also at higher risk of FTR when compared to females. Patients with the highest Elixhauser score (≥5) had a higher risk of FTR [1.44 (1.31-1.60)] when compared to patients without comorbidities. Emergent procedures also increased the

42

risk almost 6 times. Finally, when compared to gynecological procedures, all other procedures increased the risk of FTR.

Table 9: Multivariable Logistic Regression to determine Risk Factors for Failure to Rescue

PREDICTOR ADJUSTED RR 95% CI p

43 3.4 Difference-in-difference Analysis

As previously described, in order to account for the decrease in FTR due to secular trends, a DID analysis was performed to determine if the implementation of CCRTs decreased the risk of FTR independent of any secular trends. Table 10 and Figure 6 summarize the results of the DID analysis of relative risks for FTR. Considering all the different surgery types, there was no significant decrease in the FTR before and after the implementation of CCRT programs in this analysis. For the patients in the CCRT group there was a decrease in the relative risk 0.84 (0.78-0.90); however, this decrease was also observed in those centers that did not implement a CCRT program in the same study period, 0.85 (0.80-0.91). Therefore, the risk ratios yield no difference after the implementation of CCRTs [0.99 (0.89-1.09)].

When the analysis was performed by subgroups of surgery, a decrease in the risk ratio was observed in the orthopedic group. In this group, the effect was larger before and after the implementation of the programs in the CCRT group [RR 0.78 (0.72-0.86)] than in the non-CCRT group [RR 0.93 (0.86-1)]. This yielded a risk ratio of 0.84 (0.75-0.95) and therefore a 16%

decrease in the risk of FTR for patients who underwent an orthopedic operation after the implementation of CCRTs.

Contrary, in the group of patients who underwent a urological procedure, the risk ratio was increased [RR 1.34 (1.10-1.64)]. In this case, there was not a significant decrease in the FTR before and after implementation of the programs in the CCRT group [RR 0.96 (0.82-1.11)] and there was a significant decrease in the non-CCRT group [RR 0.71 (0.62-0.82)]. In the rest of the surgical subgroups there was no effect of CCRT implementation in the risk of FTR.

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Table 10: Difference-in-Difference Analysis for the Risk of Failure to Rescue

RR CCRT RR NON-CCRT RISK RATIO

ALL SURGERIES 0.84 (0.78-0.90) 0.85 (0.80-0.91) 0.99 (0.89-1.09) GENERAL

SURGERY

0.83 (0.75-0.92) 0.85 (0.78-0.93) 0.97 (0.85-1.11) ORTHOPEDIC 0.78 (0.72-0.86) 0.93 (0.86-1.00) 0.84 (0.75-0.95) GYNECOLOGY 0.89 (0.55-1.44) 0.81 (0.53-1.22) 1.10 (0.57-2.11)

THORACIC 0.79 (0.70-0.90) 0.62 (0.38-0.99) 1.29 (0.78-2.1)

UROLOGY 0.96 (0.82-1.11) 0.71 (0.62-0.82) 1.34 (1.1-1.64)

VASCULAR 0.96 (0.85-1.1) 0.99 (0.8-1.23) 0.97 (0.76-1.23)

Figure 7. Forest plot of Relative Risks for Failure to Rescue According to Difference-in-Difference Analysis

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3.5 Subgroup Analysis of Failure to Rescue in Patients Treated Electively and Emergently A total of 109,225 patients were treated electively and 39,657 were treated emergently.

Table 11 summarizes the risk of FTR among patients who underwent an elective surgical procedure. After adjusting for confounders, similar results were found as when the whole cohort was analyzed. The same was true when analyzing the group of patients that underwent surgery emergently. These results (for those patients operated emergently) are summarized in Table 12.

With regards to the difference-in-difference analysis, among those patients treated electively, CCRTs did not seem to decrease the risk of FTR after their implementation. The RR in the CCRT group was 0.74 (0.67-0.80) while the RR for the non-CCRT group was 0.65 (0.58-0.73). The risk ratio was therefore 1.13 (0.97-1.31). When the group of patients treated emergently was analyzed, the RR in the CCRT group was 0.89 (0.83-0.96) and in the non-CCRT it was 0.95 (0.88-1.01), yielding a risk ratio of 0.94 (0.85-1.05). Therefore, in this subgroup of patients, CCRT implementation did not seem to impact the risk of FTR.

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Table 11: Multivariable Logistic Regression to determine Risk Factors for Failure to Rescue in Patients Undergoing an Elective Procedure

PREDICTOR ADJUSTED RR 95% CI p

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Table 12: Multivariable Logistic Regression to determine Risk Factors for Failure to Rescue in Patients Undergoing an Emergent Procedure

PREDICTOR ADJUSTED RR 95% CI p

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3.6 Sensitivity Analysis in Patients Treated in Academic or Non-Academic Institutions with

≥100 beds

A sensitivity analysis was done, restricting the analysis to patients treated in large institutions. There was a total of 122,313 patients treated in academic or non-academic large (≥100 beds) institutions.

The risk factors for FTR in this population are summarized in Table 13. There was no major difference when compared to the analysis of the whole cohort.

Table 14 and Figure 7 summarize the results of the DID estimate of this sensitivity analysis. As previously shown, the DID analysis revealed no decrease in the risk of FTR after the implementation of CCRT [risk ratio 1.01 (0.89-1.14)]. However, a similar effect was seen in patients who underwent an orthopedic intervention [risk ratio 0.82 (0.71-0.95)] and in those undergoing a urological intervention [risk ratio 1.35 (1.09-1.68)]. Therefore, the sensitivity analysis with a different cohort revealed similar results validating our initial analysis.

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Table 13: Multivariable Logistic Regression to determine Risk Factors for Failure to Rescue in the Sensitivity Analysis

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Table 14: Sensitivity Analysis Among Patients Treated in Academic Institutions or in Hospitals with >100 beds. Difference-in-difference estimate

RR CCRT RR NON-CCRT RISK RATIO

ALL SURGERIES 0.84 (0.79-0.90) 0.83 (0.76-0.92) 1.01 (0.89-1.14)

GENERAL SURGERY

0.83 (0.75-0.92) 0.83 (0.73-0.93) 1.00 (0.86-1.18)

ORTHOPEDIC 0.78 (0.71-0.86) 0.95 (0.86-1.06) 0.82 (0.71-0.95)

GYNECOLOGY 0.89 (0.54-1.46) 0.57 (0.38 -0.86) 1.55 (0.81-2.98)

THORACIC 0.79 (0.69-0.89) 0.59 (0.35-0.99) 1.33 (0.78-2.28)

UROLOGY 0.95 (0.81-1.10) 0.70 (0.59-0.82) 1.35 (1.09-1.68)

VASCULAR 0.93 (0.83-1.05) 0.99 (0.78-1.25) 0.94 (0.72-1.23)

Figure 8: Sensitivity Analysis Forest Plot

51 4. DISCUSSION

4.1 Summary of findings

A total of 810,279 patients were identified as having a major surgical procedure in Ontario during the study period. Of these patients, 148,882 (18.4%) developed a postoperative complication and 13,659 died within 30 days. The proportion of FTR was 9.2% among surgical patients in Ontario. The implementation of CCRTs did not reduce the risk of FTR among these patients by using a DID analysis which considered the secular trends in the incidence of FTR.

The risk ratio for all types of surgical procedures included in the study was 0.99 (0.89-1.09).

When patients were stratified by type of surgical procedure, the risk ratio for those patients undergoing an orthopedic intervention was 0.84 (0.75-0.95) and therefore in this patient population there was a 16% risk reduction in FTR after the implementation of CCRT adjusting for confounders and for secular trends. On the other hand, patients who underwent a urological intervention, were found to have a risk ratio of 1.34 (1.1-1.64). In a multivariable analysis, risk factors for FTR were age, sex, Elixhauser comorbidity score, type of surgery, emergent procedure, previous cancer history and income. All these results were consistent in sensitivity and subgroup analyses.

4.2 Patient cohort characteristics

This is the first study to analyze the impact of CCRTs in the outcome of surgical patients who develop a surgical complication. Specifically, this is the first study examining the association between the implementation of CCRTs and FTR. Several previous attempts to study the impact that these programs may have on patients’ outcomes have included both medical and surgical patients with and without complications34,37,53-57

. Therefore, the hypothesis that CCRTs may decrease FTR had never been tested. Our study population was large and represented a

52

broad cross section of representative surgical procedures performed in the province of Ontario.

Given that CCRTs were first implemented in large academic institutions, it was no surprise to see that patients in the non-CCRT groups were less frequently operated in academic and large institutions and that patients had more comorbidities compared to patients treated in non-CCRT hospitals. It has been well established that centralizing care in larger institutions, especially in complex patients and complex interventions improves outcomes^58-63 and this has been the general practice. The same is true with care centralization in Ontario⁶⁴ and therefore there were differences in the proportion of patients undergoing certain procedures in CCRT and non-CCRT centers. A recently published study using ICES data, studied the effect of lung cancer resection centralization in Ontario⁶⁴. As seen in the present study, more patients had lung resections in designated cancer surgery hospitals. With regards to death within 30 days, both in the CCRT group (-0.3%) and in the non-CCRT group (-0.2%) there was a decrease over time. It has already been shown in several studies that the outcomes after surgery improve over time^65-68. This was the reason to perform a DID analysis and adjust for secular trends. Indeed, without having a control group and a way to adjust for secular trends, the results of our study may have been biased towards a positive association between the implementation of CCRTs and a decrease in FTR.

4.3 Discussion of findings related to primary objective

The main finding of this study is that after adjusting for risk factors for FTR and after considering the secular trends by using a DID estimate, we found that after the implementation of CCRTs there was no decrease in the proportion of FTR. Even though it is intuitive to think that these programs should improve patient care, several studies attempting to prove this have failed32,34,45,53-56

. In many of those studies, the lack of a control group made the results difficult to

53 interpret32,34,45,53-56

. Here we tried to control for secular trends and have a control group.

Certainly, this was not perfect as there were significant differences between the study group (CCRT) and the control (non-CCRT group) as previously mentioned. It is possible then, that given that patients in the CCRT group underwent more complex operations and patients had more comorbidities, the effect of CCRT is less noticeable. On the other hand, these programs were in fact designed to improve the outcomes in such complex scenarios and for that reason were implemented first in larger institutions.

The are several potential explanations for our findings. First, even though CCRTs provide in theory a faster response to patient deterioration, the reality is that before their implementation, and in centers where this were not implemented, patients were already being taken care in surgical wards. In general, in centers without these programs, wards are supervised by registered nurses and junior surgical staff. Therefore, it is possible, that even though the response with CCRT is faster and better structured, the care given to a given patient does not differ from the ward staff treating initially a deteriorating patient and calling ICU staff for assistance in case its needed. The other possibility is that these programs take time to get implemented and therefore to demonstrate their results. To avoid this, we decided to have an 18-month period before the implementation and more than 7 years after the implementation to allow for improvement. Even with this longer follow-up period we were not able to identify differences.

One of the most salient results of our study was the outcome of the DID analysis in patients undergoing an orthopedic intervention. In this particular patient population (n=21,797) the relative risk in those patients treated in CCRT centers was 0.78 (0.72-0.86) whilst in those patients treated in non-CCRT hospitals the effect was smaller 0.93 (0.86-1.00). The risk ratio reflected a 16% reduction in FTR in this patient population after adjusting for secular trends and

54

other confounders. These results are important because it suggest that CCRTs may be more effective in a certain patient population. It is difficult to know the exact reason why this effect was seen in orthopedic patients and not in other types of surgeries. It is possible that orthopedic surgeons operate on medically complex patients, yet do not have expertise in the medical management of complex conditions. Therefore, the addition of CCRT may have had a more prominent effect among patients being cared for by orthopedic surgeons.^69,70

The effect among urology patients was different. In those patients treated in CCRT hospitals the reduction in FTR was not statistically significant 0.96 (0.82-1.11) while in the non-CCRT hospitals there was a marked reduction 0.71 (0.62-0.82). This yielded a risk ratio of 1.34 (1.1-1.64). The reason for a DID estimate of 1.34 was mainly due to a large decrease in FTR in non-CCRT hospitals and not an increase in the CCRT centers. The most plausible explanation for this finding is the fact that non-CCRT centers (smaller non-academic) have been improving in the last years their care. Centralization has also played a role in improving outcomes in Ontario⁶⁴ and it is expected that some of these patients in the non-CCRT group were treated at larger centers and therefore results were improved. However, further work should be done to investigate the exact causes for such result.

Our results are similar to some other studies that have shown no impact of CCRTs in patients’ outcomes^32,39. The main difference with those studies is the ability to control for secular trends with our approach. In fact, when the relative risks are calculated only in the group of patients treated in the CCRT centers there is a decrease in the risk of FTR overall and for most surgeries. It is the adjustment to secular trends and the existence of a control group that made the results more accurate and yielded no impact of CCRTs on FTR.

Risk factors for Failure to Rescue

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Failure to rescue is a devastating condition when a patient dies after the development of a surgical postoperative complication. In this study we identified risk factors for the occurrence of FTR. The risk factors identified were age, male sex, presence of comorbidities, type of surgical procedure, emergent intervention, previous cancer history and income quintile. Most of this risk factors are not surprising. Even though several have looked at risk factors for FTR in specific interventions, we were not able to identify a population-based study looking at different types of surgeries. Older age has already been identified as a risk factor for FTR in a study looking at patients that underwent a trauma laparotomy⁷¹. Furthermore, another study from the US that included more than 200,000 patients also identified age as an important predictor⁷². Another more recent study analyzing the impact of volumes in FTR after pancreatectomy also identified age, male sex and comorbidities as risk factors for FTR¹⁷. Other risk factors such as comorbidities are expected to increase the risk of dying after a surgical procedure^73,74. Our study results are therefore in accordance to previous literature.

Secondary outcome measures

From our secondary outcomes univariate analysis, we identified an increase in the rate of return to the OR in CCRT (3.1%) vs. non-CCRT (2.4%) groups. The most plausible explanation for this finding is the fact that CCRT may be identifying surgical complications at an earlier stage (when patient starts deteriorating) and therefore would prompt a surgical re-intervention compared to non-CCRT centers. However, this was not the main objective of this work and would need further investigation.

4.4 Rationale for using a difference-in-difference analysis

4.4 Rationale for using a difference-in-difference analysis