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Landon Stewart
Landon Stewart

Dirty Cases [v.0.1] !NEW!

There were 998 cases with a postoperative complication, representing 6.53% of the total plastic surgery procedures analyzed (Table 3). Nearly one-fifth of dirty procedures incurred a post operative complication, compared to 15.76% of contaminated cases, 9.20% of clean/contaminated, and 4.81% of clean cases (Table 3). Surgical site infections (SSIs) made up the majority of these postoperative complications, occurring in 458 cases (3.00%). Reoperation and mortality rates also increased as the wound classification transitioned from clean to dirty (Table 3).

Dirty Cases [v.0.1]

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In contrast, over 36% of dirty cases were pressure sore and flap procedures. Our total SSI rate of 3.00% (458 of 15,289 patients) is consistent with published reports in the plastic surgery literature [16-18]. As a finding from a multi-center database, our data regarding surgical infection rates may supersede the implied biases of individual surgical series based on only a few surgeons or institutions, as seen in previous studies. A prospective study conducted by Andenaes et al. [15] found that wound infection rates escalated from 10.2% in clean plastic surgery cases to 37.5% in dirty plastic surgery cases. Our overall SSI rates were much lower-ranging from 2.74% in clean procedures to 5.06% in dirty operations. The discrepancy in infection rates is most likely attributable to the fact that our study utilized a more stringent definition of infection, based on CDC criteria; the study by Andenaes et al. [15] employed a Wound Infection Score (WIS) system that included a single presentation of "edema," "redness," or "increased pain" in their definition of wound infection. Additionally, a learning curve may exist, with plastic surgeons being more selective when choosing patients for specific operations at the present time compared to the years captured by the Andenaes study.

Our results showed no observable incremental increase in infection rates when progressing from clean to dirty cases. While there does appear to be a slight correlation between wound classification and overall SSI, the relationship breaks down when stratified by type of infection. The highest rate of organ/space surgical site infections was associated with dirty procedures; however, the highest rates of superficial and deep SSIs occurred in contaminated operations. Our risk-adjusted multivariable regression model revealed that wound classification was not a significant predictor for two out of the three types of surgical site infections, namely superficial SSIs and organ/space SSIs. Contaminated and dirty wound categories were significant independent predictors of a deep SSI, associated with a nearly threefold increased risk of a deep SSI. These findings are in contrast to the study by Ortega et al. [14], which analyzed infection rates by wound class across all surgical specialties captured in NSQIP. They demonstrated an increased rate of infection when advancing from clean to dirty procedures and found wound classification to be a significant predictor of superficial, deep, and organ/space surgical site infections by multivariate analyses.

The reasoning for the lower infection rates in non-clean plastic surgery cases compared to similar cases in the Ortega study is multifaceted. Patient selection is a presumed contributor, with plastic surgeons operating on individuals who may be at a baseline lower risk for infections. Additionally, the majority of surgical site infections were classified as superficial and thus involved only skin or subcutaneous tissue. Plastic surgeons are trained in procedures involving soft tissues and therefore might have invested more effort into techniques such as debridement of contaminated or devitalized tissues, dead space reduction, and meticulous layered closure to reduce the likelihood of complications in the superficial and deep tissue layers. The discrepancies in infection rates by wound class could also be due in part to the differences in the procedure characteristics that define the nonclean classifications. In the majority of non-clean procedures in the Ortega study, the alimentary tract was entered. Plastic surgeons only occasionally enter the alimentary or respiratory tracts, so the majority of their non-clean procedures are likely to be those associated with open wounds or trauma. Moreover, these non-clean procedures may involve surgical debridement or preparation of the surgical bed as the first step of the reconstructive operation. Thus, the operation is a treatment for the contaminated wound rather than the contaminated wound being a consequence of the procedure.

Although statistical analysis did not support wound classification as a predictor of most types of surgical site infections, it was proven to be significant in predicting overall complications. In acknowledgement of the baseline discrepancies in patients with and without recorded surgical site infections, variables that reached significance on bivariate screening were included in the regression analysis. Clean/contaminated cases showed a 1.33 increased odds of having a postoperative complication (95% CI, 1.03-1.71; P=0.027), contaminated cases held a 1.9 increased odds of having a postoperative complication (95% CI, 1.44-2.52; P

Wound classification was also a significant predictor of reoperation. Contaminated operations had a 69% increased risk for reoperation, and dirty procedures had over a 100% increased risk for reoperation. Additionally, contaminated and dirty cases were significant predictors for mortality, with contaminated procedures having an OR of 6.35 and dirty procedures having an OR of 10.55. While NSQIP does not track the reason for reoperation or cause of death, these results may reflect the possibility of increased microbial contamination contributing to instances that lead to reoperation or death, such as wound break-down and sepsis. Moreover, it may be that patients in a wound classification with a higher bacterial burden have preoperative characteristics and comorbidities that place them at increased risk for such adverse events.

The wound classification system was created to have universal utility in identifying procedures with an inherently higher risk of postoperative infection. However, the large majority of plastic surgery cases are clean cases, and the definitions of the other classifications as they pertain to plastic surgery are less clear. One single institutional study of wound classification found 19% of their cases to be misclassified [20]. When the ACS-NSQIP data was narrowed to study plastic surgery alone, wound classification lost much if its significance as a predictor for surgical site infection. The classification scheme does not consider basic plastic surgical principles such as the inherent vascularity of the surgical site or the use of prosthetic material. A modified wound classification system may be necessary to strengthen its applicability to specific fields and help further standardize preoperative antibiotic regimens and surgical advancements.

Despite suggestions that the nearly 50-year-old classification system should be revised, no such modifications have been adopted [21,22]. However, Culver et al. [9] did devise the NNIS SSI risk index, which did not address the wound classification scheme itself, but combined it with other known risk factors to improve predictive value. The NNIS SSI risk index assigned point values to the following three criteria: American Society of Anesthesiologists score of 3 or greater, wound classification of either contaminated or dirty, and procedure-specific excessive operative time. The risk index proved to be a better predictor of SSIs than wound classification alone. Optimally, however, a wound classification system could serve as a tool for risk-adjustment specific to the wound characteristics, e.g., size, location, depth, approach, and level of contamination, and would be independent of patient and other operative variables. We therefore suggest that any future modification of the wound classification system consider such focused variables as anticipated incision size, detailed anatomic locations, and surgical approach (laparoscopic versus open) in addition to the level of contamination to better predict infections.

In non-health care settings, sodium hypochlorite (bleach / chlorine) may be used at a recommended concentration of 0.1% or 1,000ppm (1 part of 5% strength household bleach to 49 parts of water). Alcohol at 70-90% can also be used for surface disinfection. Surfaces must be cleaned with water and soap or a detergent first to remove dirt, followed by disinfection. Cleaning should always start from the least soiled (cleanest) area to the most soiled (dirtiest) area in order to not spread the dirty to areas that are less soiled.

You might see an unexpected failover or a blue diagnostic screen when both vSphere FT and a GART are enabled in a guest OS due to a race condition. vSphere FT scans the guest page table to find the dirty pages and generate a bitmap. To avoid a conflict, each vCPU scans a separate range of pages. However, if a GART is also enabled, it might map a guest physical page number (PPN) to an already mapped region. Also, multiple PPNs might be mapped to the same BusMem page number (BPN). This causes two vCPUs to write on the same QWORD in the bitmap when they are processing two PPNs in different regions.

In the AMD IOMMU interrupt remapper, IOAPIC interrupts the use of an IRTE index equal to the vector number. In certain cases, a non-IOAPIC interrupt might take the index that an IOAPIC interrupt needs.

The Host name or IP Network uplink redundancy alarm reports the loss of uplink redundancy on a vSphere standard or a distributed switch for an ESXi host. The redundant physical NICs are either down or are not assigned to the switch. In some cases, when more than one VMNIC is down, the alarm resets to Green even when one of the VMNICs is up, while others might still be down.

In some cases, ESXi hosts running ESXi 6.7 Update 3 might take long to complete tasks such as entering or exiting maintenance mode or connecting to a vCenter Server system. The delay in response might take up to 30 min. This happens when the CIM service tries to refresh periodically the storage and numeric sensors data under a common lock, causing the hostd threads to wait for response. 041b061a72


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