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Get Form. How it works Open form follow the instructions. Easily sign the form with your finger. Rate form 4. Handy tips for filling out Braden Scale Pdf online Printing and scanning is no longer the best way to manage documents. Quick steps to complete and eSign Braden Scale Pdf online: Use Get Form or simply click on the template preview to open it in the editor. Start completing the fillable fields and carefully type in required information. Use the Cross or Check marks in the top toolbar to select your answers in the list boxes.
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Add a legally-binding eSignature. Content and construct validity of the Braden Scale were established by expert opinion and empirical testing. Two prospective studies of predictive validity were completed to determine the scale's sensitivity and specificity. Predictive validity was calculated for each cut-off point of the scale.
Nurs Res. Validity of the Braden Scale in grading pressure ulcers in trauma and burn patients. J Surg Res. Bergstrom N, Braden B.
A prospective study of pressure sore risk among institutionalized elderly. J Am Geriatr Soc. Predicting pressure ulcer risk: a multisite study of the predictive validity of the Braden Scale. Bergstrom N, Braden BJ. Predictive validity of the Braden Scale among Black and White subjects.
Predictive validity of the Braden Scale and nurse perception in identifying pressure ulcer risk. Appl Nurs Res. Lindgren, M; Unosson, M. A risk assessment scale for the prediction of pressure sore development: reliability and validity. Journal of Advanced Nursing.
Although standard survival analysis is commonly used to account for censoring, it assumes that the censoring is noninformative. The cause-specific hazard regression represents the instantaneous rate of the outcome in patients who have not experienced a HAPI.
Although the cause-specific Cox model can handle competing risks, it requires independent observations. However, the data for this study consists of nonindependent episodes the outcomes for patients in the same hospital are more likely to be similar than those in a different hospital, and patients may experience multiple hospitalizations during the study period.
We first fitted a cause-specific Cox proportional hazard fixed effects model, ignoring facility effects. The facility effects were defined as the exponentiated estimates of the normal random effects from the model.
Episodes were ascribed to the admitting facility prognostic approach. Last, we evaluated the effect of having multiple episodes per patient by refitting the model to a data set containing one randomly selected episode per patient and comparing the results. The results described in this article are based on the full data set. Model covariates were selected using a redundancy analysis approach Harrell, A redundancy analysis is a rigorous approach to data reduction that involves removing predictors that are easily predicted from other predictors by using flexible parametric additive regression models.
We evaluated possible nonlinear splines and polynomial effects and two-factor interactions by comparing the log likelihood of more complex models than a linear main-effects-only model.
Performance of the mixed effects model based on the full data set was evaluated using the c -statistic and Cox and Snell pseudo R 2 measures. In addition, the c -statistic, R 2 , and calibration of the fixed effects version of the model were evaluated using 1, bootstrap samples Harrell, In this study, , patients who experienced , inpatient hospital stays at the 35 hospitals during the study period were identified.
During the concatenation process, episodes where the initial hospital stay occurred at a non-KP hospital were excluded. The final study cohort consisted of , patients who experienced , inpatient episodes.
Of these episodes, 6, involved interhospital transport to a nonsystem hospital. Table 1 summarizes key episode characteristics. There were HAPI episode characteristics are summarized in Table 2.
There were 1, HAPI episodes within this cohort, resulting in a rate of 0. The average length of stay until the first HAPI development was A visual display of the cumulative incidence graph for a HAPI over 30 days of hospitalization is seen in Figure 1.
Examples of medical devices associated with HAPI formation were bilevel noninvasive positive pressure breathing masks, endotracheal tubes, nasogastric tubes, and nasal cannula oxygen tubing. Cumulative incidence plot for hospital-acquired pressure injury hazard over 0—30 days of hospitalization. The average lowest total Braden Scale score within the first 24 hours was The average LAPS2 score was Hazard ratio reflects the impact of the respective variable on the risk for HAPI over time when there is change from Q1 to Q3.
Therefore, HR results in Table 4 and Figure 2 reflect the impact of a continuous variable on the increased rate of HAPI over time when changed from Q1 25th percentile to Q3 75th percentile; Harrell, The main effects model was not substantially different from the more elaborate models.
The facility random effect variance 0. The analysis based on the data set containing one randomly selected episode per patient yielded similar results. Risk-adjusted hospital effect for hospital-acquired pressure injury HAPI.
The x-axis shows individual hospitals, and the y-axis shows the risk-adjusted hospital random effect on the probability of experiencing a HAPI for each hospital vertical bars. The horizontal lines denote 1 SD marks. We have described risk-adjusted variation in HAPI incidence and hazard risk for HAPI over time within a hospital inpatient cohort, resulting in one of the largest studies both with the number of episodes and hospitals to date in the era of comprehensive EMRs.
Although comprehensive EMRs have become more common, the fact remains that many hospitals still cannot extract and format granular clinical data. Thus, our study is valuable because it includes multiple individual HAPI risk factors in conjunction with severity of illness and longitudinal comorbidity scores as well as HAPI risk-specific Braden Scale scores. Another valuable aspect of this study was the utilization of a cause-specific Cox proportional hazard model.
This model emphasizes the effect of the variable on a specific outcome through censoring competing events. Not censoring can lead to overestimation of cumulative incidence Austin et al. Although a cause-specific model is not best suited for estimation of individual risk predicting a given outcome at a given time , it does promote etiology—where HRs can be used to estimate an effect size Lau et al.
The overall HAPI incidence rate 0. The rate of 2. The HAPI rate of 3. Despite the integrated nature of this hospital system and a low rate for HAPI, the analyses showed substantial residual interhospital variation in HAPI incidence.
This variation is remarkable given the set of variables included in the model e. This suggests that further research is still needed to understand between-hospital variation. For example, examination of variation in HAPI stages and types device related or not , specific comorbidities, and hospital setting intensive care unit vs.
Such work could include examining why, given identical risk factors, some patients develop HAPIs in one hospital but not in another. For this study, age and gender were significant in the model, with results aligning with previous studies. Age and gender are immutable variables, yet knowledge of the association with HAPI assists clinicians in recognizing patients at risk within the first hours of admission. Importantly, the Braden Scale—in use for 30 years— remains a predictor of HAPI risk even after controlling for multiple other factors.
In this study, the lowest Braden Scale total score in the first 24 hours of admission—when changed from 13 Q1 to 18 Q3 —was a significant protective factor of HAPI over time.
These results align with previous HAPI studies in which lower scores equate to a higher risk Bergstrom et al. Although only Braden Scale total scores were used in our study, other investigators have reported similar results with specific subscales; for example, immobility scores have been associated with HAPI Cox, ; Raju et al.
In a systematic review and meta-analysis of 20 studies, Braden Scale scores showed sensitivity The Braden Scale is an embedded part of nursing practice for many healthcare organizations in the United States, with total and subscale scoring directing preventive interventions.
The Braden Scale captures multiple domains such as activity, sensation, and mobility that are not captured by other predictors in our model. Thus, additional research on the predictive capacity of Braden Scale subscales in large data sets may yield evidence for enhanced customization of preventive interventions.
The LAPS2 severity-of-illness score—in our study, change from 55 Q1 to Q3 —was also a independently significant predictor.
Hatanka et al. Associations have also been shown using composite severity-of-illness scores Manzano et al. In contrast, the LAPS2 is calibrated for all hospitalizations and captures the combined effects of 16 laboratory test results, vital signs, neurological status, and pulse oximetry. One recent large sample cohort study covering 15 hospitals did show the combination of age, body mass index, and Charlson Comorbidity Index as associated with HAPI risk Gardiner et al.
There were smaller prediction errors when using the COPS2. In addition, the COPS2 had several other advantages in this study: It is based on 12 months of data, it is more granular, and it is now generated monthly on all KP adults in California.
An unexpected finding in this study was that nonsurgical admissions through the emergency department had significantly less HAPI risk over time when compared with other admission categories.
This category includes example diagnoses of pneumonia, chest pain, congestive heart failure, and unspecified septicemia. We can speculate that surgical patients may have a higher risk because of immobility before and after surgery or from the length of time in the operating room for the surgical procedure.
Further research is needed. For the application of study findings at the clinical level, results could lead to EMR-based systems that prompt healthcare providers to consider the combined effects of age, type of admission, severity-of-illness scores LAPS2 , and comorbidity burden COPS2 —in addition to low Braden Scale scores at the time of admission—to identify patients at a high HAPI risk.
One important implication of our work is that our approach could be extended to other areas. Another recommendation is to explore the predictive capability of the Braden Scale total score and subscales with respect to outcomes such as mortality and length of stay. Collaborative studies regarding careful examination of the process of care at hospitals with high and low adjusted HAPI rates could yield new insights.
There are limitations to the analyses.
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