Acute Pancreatitis Clinical Activity Index | Free Essay Example

Acute Pancreatitis Clinical Activity Index

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Topic: Health & Medicine
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Abstract

Acute pancreatitis is a serious condition with increasing cases of hospital readmission following 30 days after discharge from inpatient settings. As such, interventions have been formulated and implemented to reduce cases of readmission. In this report, Acute Pancreatitis Clinical Activity Index (AP-CAI) was presented as an evidence-based, disease-specific intervention tool for safe discharge of patients to reduce instances of readmission within 30 days after discharge. AP-CAI showed that many patients were discharged with an active state of acute pancreatitis, implying that readmission was imminent.

With the implementation of the tool, care providers could assess patients’ conditions with real-time data and understand the disease trajectory during the hospitalization period. AP-CAI is safe, easy to use, and clinically reliable means of objectively assessing the optimal time for discharging a patient. It is however observed that AP-CAI lacks optimal cut off for different populations, which should be determined. Additionally, available evidence suggests that some of these intervention predictive tools perform poorly and, therefore, not reliable. Overall, AP-CAI is presented as a reliable intervention tool for the safe discharge of acute pancreatitis patients to reduce readmission.

Critical Analysis of the Acute Pancreatitis Clinical Activity Index (AP-CAI) Intervention Tool

Acute pancreatitis, a common inflammatory disorder of the pancreas, is a major cause of hospitalization for gastrointestinal disorders in the US and other parts of the world (Beyer, Simon, Mayerle, & Lerch, 2016). It accounts for over 280,000 hospitalizations every year (Wu & Banks, 2013). In 2010, it was estimated the average length of stay across the US hospitals was five days with an annual cost of $2.9 billion. Its mortality rate ranged between 3% and 15%. These increments have led to demands for effective management of acute pancreatitis. Acute pancreatitis normally results from an alcoholic binge or gallstones. Patients present with this condition have severe abdominal pain in the upper mid-abdomen usually accompanied by nausea and vomiting.

While most cases do not lead to complications, some patients will need intensive medical care or eventually develop long-term pancreatitis. For long-term cases of acute pancreatitis, hospital readmission becomes common. It has been observed that approximately one in five patients hospitalized for acute pancreatitis (AP) requires unplanned readmission within 30 days of discharge (Whitlock et al., 2010). Additionally, it is known that under the Affordable Care Act of 2010, the Hospital Readmissions Reduction Program requires reduced payments for hospitals with relatively high costs of readmissions (Cronin, Greenwald, Crevensten, Chueh, & Zai, 2014). Some studies have also demonstrated the failure to comply with evidence-based recommendations for the management of cases of acute pancreatitis (Greenberg et al., 2016). This report aims to present the Acute Pancreatitis Clinical Activity Index (AP-CAI) as an intervention tool that can predict and reduce 30-day readmission after acute pancreatitis inpatient discharge.

The Acute Pancreatitis Clinical Activity Index (AP-CAI)

According to the developers of the AP-CAI, readmission within 30 days following hospitalization for acute pancreatitis is a strong indicator of death within a year. Based on this observation, Michael Quezada and his colleagues recently developed the AP-CAI as a robust disease-specific clinical evaluation tool for acute pancreatitis for safe discharge of patients to reduce readmission. Although AP-CAI is still under validation by experts, it is has been presented as an objective assessment tool for acute pancreatitis. It has been used to identify the minimum score for safe discharge of patients with acute pancreatitis from inpatient environments. According to Quezada, the AP-CAI scores are derived after 12-hour durations in which the optimal and aggregate measurements are applied in determining a patient’s score. The tool assesses organ failure, systemic inflammatory response syndrome (SIRS), morphine comparable dose, abdominal pain, and ability to tolerate solid diets.

AP-CAI Empirical Assessment

The AP-CAI was developed following a study conducted among patients admitted with acute pancreatitis at the LAC and USC Medical Center. It was conducted between March 2015 and March 2016 (Altersitz, 2016). The scores were only based on the outcome of the first admission and were analyzed in retrospect.

The main outcome was readmission of patients with deteriorating pancreatitis signs, management of complications, and/or related therapies for pancreatitis within 30 days following the first discharge. The AP-CAI data showed that patients were being discharged with active acute pancreatitis, especially at the large urban hospital responsible for the majority of underserved patients. It was observed that AP-CAI score beyond 50 during discharge was majorly linked to inpatient readmission and presentation to the emergency department within 30 days following the discharge from and admission for the disease. Other confounding variables, such as gender, race, age, and severity of the condition were already controlled.

Participants were 332. Noted cases of gallstone were 149, alcohol was 84, and other conditions represented 99. Reported cases of acute pancreatitis readmission accounted for 18% of the participants. Based on the AP-CAI, patients who had a score greater than 50 were 114 out of which 25 patients were readmitted, and another 13 presented to the emergency department. Among patients (218) who had a score of less than 50, four patients were readmitted and three presented to the emergency department. Further, it was observed that 72% of patients readmitted within 30 days had severe pancreatitis signs, four patients had pancreatitis complications and a similar number had therapy-related complications. Of patients who presented to the emergency department, 16 had deteriorating or smoldering symptoms.

It was established that AP-CAI score is easy to calculate and could be clinically applied in deciding the suitability to discharge patients following acute pancreatitis hospitalization. Additionally, the AP-CAI could also be useful for determining the most suitable time for specific interventions, including the reintroduction of regular diets and fluid resuscitation.

Researchers also demonstrated that patients with an AP-CAI score of more than 50 were almost nine times as likely to be readmitted within 30 days after the discharge while patients with past acute pancreatitis admission were thrice likely to be readmitted (Altersitz, 2016). Further, following a multivariate analysis, patients who had more than a score of 50 were ten times more likely to be readmitted while patients with past admission maintained their possibilities of readmission.

Based on univariate analysis for patients who presented to the emergency department, patients who had AP-CAI score exceeding 50 were almost nine times more likely to be readmitted. Only a score of more than 50 was identified as a predictor. When the multivariate analysis was applied, patients were only eight times likely to be readmitted.

One major weakness of the AP-CAI is that there is no determined optimal cut off for diverse populations. Hence, users of this interventional tool need to determine it. Nevertheless, the tool offers a method of objectively evaluating the condition of acute pancreatitis in real-time and noting score progresses for patients across hospitalization periods. Additionally, the AP-CAI could offer a more reflective assessment of underlying acute pancreatitis activity relative to other past scoring tools.

Supporting Literature

Notably, a growing body of research aims to explain and validate various tools used for predicting risks of hospital readmission (Kansagara et al., 2011). Interests in hospital readmission predictive tools are driven by some factors. First, it is observed that transitional care interventions tend to reduce cases of readmission in patients with long-term conditions (Kansagara et al., 2011). Readmission risk evaluation helps care providers to focus on patients at greater risks and require resource-intensive interventions.

Preferably, predictive models designed for patient assessment could offer clinically useful data of readmission risk and provide information early enough during hospitalization for subsequent transitional care management, especially discharge management before hospital inpatient discharge. Readmission data are also used for quality assessment. Specifically, the Centers for Medicare & Medicaid Services (CMS) introduced penalties for hospitals that had high cases of risk-standardized readmission cases (Kansagara et al., 2011; Cronin et al., 2014). Appropriate risk adjustment techniques are applied to determine risk-standardized rates, which may eventually be used for comparison, reporting, and payment determinations. In this case, such intervention tools should, therefore, have good predictive capabilities, used in larger populations, apply reliable data, use clinically accessible data, and they are readily validated among the target participants.

Although the AP-CAI intervention tool is relatively new, numerous studies exist to demonstrate the importance of predictive models in determining readmissions within 30 days of discharge (Cronin et al., 2014). Some studies have concentrated on specific disease cohorts, such as pneumonia, heart diseases, and other acute conditions when developing intervention tools for assessing risks of readmission (Hao et al., 2015). Additionally, they may also focus on specific age or veterans, for instance. According to Hao et al. (2015), such intervention models have some limitations because they cannot account for various populations, including “all payers, all diseases, and all demographics” (Hao et al., 2015, p. e0140271).

Further, most of these past studies did not demonstrate probable testing and validation but reported findings based on retrospective cohorts only (Hao et al., 2015). As such, most models are restricted for population health, and case management intended to curtail the rate of readmission in vulnerable people. Hao et al. (2015) opted for models that are more robust based on variability in research techniques and outcomes concerning the development of 30-day risk models.

Some researchers have noted inconsistent results across different 73 risk predictive models (Zhou, Della, Roberts, Goh, & Dhaliwal, 2016). It is imperative to identify the possible use, outcomes, and the type available data source before selecting a model. Given the observed inconsistent, models should be vigorously selected to prevent any possible readmission. It is also important to recognize that some models may lack sufficient data to support their applications (Zhou et al., 2016).

Evidence supports the use of predictive models to reduce cases of unplanned hospital readmission following a discharge (Zhou et al., 2016). Such models help care providers to identify patients at high risk for readmission. Interventions offer preventive models, which can be developed and used for specific patients’ identification who are at higher risks for readmission. However, it is imperative to point out that the performances of these intervention tools could be poor. Zhou et al. (2016) showed that such predictive models may not be consistent during validation and their performances differ significantly. Kansagara, et al. (2011) also observed that most available readmission risk prediction tools, irrespective of the design and purpose, perform poorly. However, such tools may be equally useful in some settings and, thus, it is necessary to improve their performances as applications become more common.

Specifically for acute pancreatitis, Whitlock et al. (2011) claimed that little evidence was available about clinical predictors of early readmission following discharge. Consequently, the researchers developed a scoring system that they concluded could identify patients with acute pancreatitis at risk for readmission. The model showed an accurate prediction of potential cases for readmission. Such predictive models are driven by the need to reduce cases of readmission of acute pancreatitis and ensure effective management of patients. Also, predictive models offer opportunities for care providers to review how to manage acute pancreatitis, including evaluating diagnosis, differential diagnosis, complicating factors, prognostic factors, treatment options, and prevention of further readmission.

Reducing multiple cases of readmission of patients following a discharge could enhance the quality of care and cut related costs (Marwah, Singla, Singh, & Sharma, 2017). It is imperative to recognize that readmission predictive tools offer some valuable lessons for developers and users. Notably, effective implementation of such tools remains vital for positive outcomes based on a 30-day readmission prediction. AP-CAI shows that risk scores must be determined within 12 hours for all inpatients following admission. Cronin et al. (2014) support the daily calculation of a risk score. The timing for risk assessment is important because of the post-discharge management of patients. In that case, reducing readmission must be assessed immediately following a patient’s admission.

Predictive models tend to eliminate some variables, such as length of hospital stay and discharge diagnosis, which are usually not available when patients are admitted. Such intervention tools should be easy to use and calculate scores. That is, developers must eliminate unnecessary complex features and restrict them to only technical capabilities. Predictive models should be relevant to users. That is, data used for assessment should be clinically or medically relevant. Zhou et al. (2014), for instance, noted that there was an ongoing debate about whether data used for developing disease-specific intervention tools should be administrative or clinical/medical data from patients’ records. Diverse sources of information could affect predictive intervention tools during validation because such tools may fail to make clinical sense to users. AP-CAI is developed to ensure that clinical data (real-time data) are captured and easy to use with limited technical capabilities.

Conclusion

This report has critically analyzed AP-CAI as an evidence-based intervention tool to reduce cases of readmission of acute pancreatitis patients following 30 days after discharge. Hence, it is presented as a safe discharge management tool. AP-CAI is disease-specific, easy to use, and developed from real-time clinical data rather than administrative data. This intervention tool offers an objective assessment of the condition of a patient in real-time and shows the score’s progress across various periods during hospitalization. The tool is clinically significant for identifying a suitable time for discharging acute pancreatitis patients from an inpatient setting safely to reduce readmission.

Notably, this intervention tool does not have optimal cut-offs for different populations, and other studies have shown that predictive tools tend to perform poorly in some settings and, thus, unreliable. Nevertheless, AP-CAI could be useful in reducing cases of readmission following acute pancreatitis treatment.

References

Altersitz, K. (2016). AP-CAI offers objective threshold for discharge after pancreatitis. Web.

Beyer, G., Simon, P., Mayerle, J., & Lerch, M. M. (2016). Mistakes in the management of acute pancreatitis and how to avoid them. UEG Education, 16, 27–30.

Cronin, P. R., Greenwald, J. L., Crevensten, G. C., Chueh, H. C., & Zai, A. H. (2014). Development and implementation of a real-time 30-day readmission predictive model. AMIA Annual Symposium proceedings, 2014, 424–431.

Greenberg, J. A., Hsu, J., Bawazeer, M., Marshall, J., Friedrich, J. O., Nathens, A.,… McLeod, R. S. (2016). Clinical practice guideline: management of acute pancreatitis. Canadian Journal of Surgery, 59(2), 128–140. Web.

Hao, S., Wang, Y., Jin, B., Shin, A. Y., Zhu, C., Huang, M.,… Fu, C. (2015). Development, Validation and Deployment of a Real Time 30 Day Hospital Readmission Risk Assessment Tool in the Maine Healthcare Information Exchange. PLoS ONE, 10(10), e0140271. Web.

Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., & Kripalani, S. (2011). Risk prediction models for hospital readmission: A systematic review. Journal of the American Medical Association, 306(15), 1688–1698. Web.

Marwah, S., Singla, P., Singh, M., & Sharma, H. (2017). Risk factors for 30-day unplanned readmission among patients undergoing laparotomy for perforation peritonitis. International Surgery Journal, 4(2), 637-644. Web.

Whitlock, T. L., Repas, K., Tignor, A., Conwell, D., Singh, V., Banks, P. A., & Wu, B. U. (2010). Early readmission in acute pancreatitis: Incidence and risk factors. American Journal of Gastroenterology, 105, 2492-2497. Web.

Whitlock, T. L., Tignor, A., Webster, E. M., Repas, K., Conwell, D., Banks, P. A., & Wu, B. U. (2011). A scoring system to predict readmission of patients with acute pancreatitis to the hospital within thirty days of discharge. Clinical Gastroenterology and Hepatology, 9(2), 175–180. Web.

Wu, B. U., & Banks, P. A. (2013). Clinical management of patients with acute pancreatitis. Gastroenterology, 144(6), 1272-81. Web.

Zhou, H., Della, P. R., Roberts, P., Goh, L., & Dhaliwal, S. S. (2016). Utility of models to predict 28-day or 30-day unplanned hospital readmissions: An updated systematic review. BMJ Open, 6, e011060. Web.