Development of a validation algorithm for 'present on admission' flagging

Accuracy assessment of patient safety incident (PSI) codes and present-on-admission (POA) indicators: a cross-sectional analysis using the Patient Safety Incidents Inquiry (PSII) in Korea

BACKGROUND

Among the various methods used, administrative data collected for claims and billing purposes, such as diagnosis codes and present-on-admission (POA) indicators, can easily be employed to assess patient safety status. However, it is crucial that administrative data be accurate to generate valid estimates of adverse event (AE) occurrence. Thus, we aimed to evaluate the accuracy of diagnosis codes and POA indicators in patients with confirmed AEs in the hospital admission setting.

METHODS

We analysed the diagnosis codes of 1,032 confirmed AE cases and 6,754 non-AE cases from the 2019 Patient Safety Incidents Inquiry, which was designed as a cross-sectional study, to determine their alignment with the Korean Patient Safety Incidents (PSIs) Code Classification System. The unit of analysis was the individual case rather than the patient, because two or more AEs may occur in one patient. We examined whether the primary and secondary diagnostic codes had PSIs codes matching the AE type and checked each PSI code for whether the POA indicator had an 'N' tag. We reviewed the presence of PSI codes in patients without identified AEs and calculated the correlation between the AE incidence rate and PSI code and POA indicator accuracy across 15 hospitals.

RESULTS

Ninety (8.7%) of the AE cases had PSI codes with an 'N' tag on the POA indicator compared to 294 (4.4%) of the non-AE cases. Infection- (20.4%) and surgery/procedure-related AEs (13.6%) had relatively higher instances of correctly tagged PSI codes. We did not identify any PSI codes for diagnosis-related incidents. While we noted significant differences in AE incidence rates, PSI code accuracy, and POA indicator accuracy among the hospitals, the correlations between these variables were not statistically significant.

CONCLUSION

Currently, PSI codes and POA indicators in South Korea appear to have low validity. To use administrative data in medical quality improvement activities such as monitoring patient safety levels, improving the accuracy of administrative data should be a priority. Possible strategies include targeted education on PSI codes and POA indicators and introduction of new evaluation indicators regarding the accuracy of administrative data.

Longitudinal evaluation of an electronic audit and feedback system for patient safety in a large tertiary hospital setting

Objective: This study sought to assess the impact of a novel electronic audit and feedback (e-A&F) system on patient outcomes. Methods: The e-A&F intervention was implemented in a tertiary hospital and involved near real-time feedback via web-based dashboards. We used a segmented regression analysis of interrupted time series. We modelled the pre-post change in outcomes for the (1) announcement of this priority list, and (2) implementation of the e-A&F intervention to have affected patient outcomes. Results: Across the study period there were 222,792 episodes of inpatient care, of which 13,904 episodes were found to contain one or more HACs, a risk of 6.24%. From the point of the first intervention until the end of the study the overall risk of a HAC reduced from 8.57% to 4.12% - a 51.93% reduction. Of this reduction the proportion attributed to each of these interventions was found to be 29.99% for the announcement of the priority list and 21.93% for the implementation of the e-A&F intervention. Discussion: Our findings lend evidence to a mechanism that the announcement of a measurement framework, at a national level, can lead to local strategies, such as e-A&F, that lead to significant continued improvements over time.

Using Potentially Preventable Severe Maternal Morbidity to Monitor Hospital Performance

BACKGROUND

The Centers for Disease Control and Prevention (CDC) measure of severe maternal morbidity (SMM) quantifies the burden of SMM but is not restricted to potentially preventable SMM. The authors adapted the CDC SMM measure for this purpose and evaluated it for use as a hospital performance measure.

METHODS

Guidelines for defining performance SMM (pSMM) were (1) exclusion of preexisting conditions from outcome; (2) exclusion of inconsistently documented outcomes; and (3) risk adjustment for conditions that preceded hospitalization. California maternal hospital discharge data from 2016 to 2017 were used for model development, and 2018 data were used for model testing and evaluation of hospital performance. Separate models were developed for hospital types (Community, Teaching, Integrated Delivery System [IDS], and IDS Teaching), generating model-based expected pSMM values. Observed-to-expected (O/E) ratios were calculated for hospitals and used to categorize them as overperforming, average performing, or underperforming using 95% confidence intervals. Performance categories were compared for pSMM vs. CDC SMM (excluding blood transfusion).

RESULTS

The overall 2016-2018 pSMM rate was 0.44%. All hospital types had over- and underperformers, and the proportions of Community, Teaching, IDS, and IDS Teaching hospitals whose performance differed from their performance on the CDC SMM measure were 12.1%, 25.0%, 38.9%, and 66.7%, respectively.

CONCLUSION

The rate of potentially preventable SMM as defined by pSMM (0.44%) was less than half the previously published rate of CDC SMM (1.03%). pSMM identified differences in performance across hospitals, and pSMM and CDC SMM classified hospitals' performances differently. pSMM may be suitable for hospital comparisons because it identifies potentially preventable, hospital-acquired SMM that should be responsive to quality improvement activities.

Development of the Korean Patient Safety Incidents Code Classification System

OBJECTIVES

Attempts to understand patient safety using administrative data in Korea have been rare. This study develops a Korean patient safety incident code classification system and identifies its characteristics to boost diagnosis code usage for assessing patient safety.

METHODS

Based on existing literature, we selected Korean Standard Classification of Diseases 7 codes for characterizing patient safety incidents using diagnosis codes. We conducted 2 rounds of review to evaluate the codes applicability to different patient safety incidents using the Delphi method. The verified diagnosis codes were then classified by incident type.

RESULTS

Of the 54,259 Korean Standard Classification of Diseases 7 codes, 4509 were applicable for Korean patients, which were divided into 2435 code groups and 2074 candidate groups. The codes were classified into 6 categories (diagnosis, medication, patient care, operation or procedure, infection related, and other) and then further classified into 35 subcategories. The major categories of patient safety incidents, in the order of frequency, involved medication, fluid and blood related (1719, 38.1%), operation and procedure related (1339, 29.7%), and patient care related (991, 22.0%). Meanwhile, there were only 2 codes related to diagnosis.

CONCLUSIONS

Our study provides a basis for estimating patient safety incidents using diagnosis codes. We suggest that gradually increasing the utilization and accuracy of the patient safety incident codes will help develop effective patient safety indicators in Korea similar to other countries. Moreover, clinicians are also needed to be aware of using the developed code classification system.

Feasibility of Capturing Adverse Events From Insurance Claims Data Using International Classification of Diseases, Tenth Revision, Codes Coupled to Present on Admission Indicators

OBJECTIVE

The aim of the study was to investigate the feasibility of using administrative data to screen adverse events in Korea.

METHODS

We used a diagnosis-related groups claims data set and the information of the checklist of healthcare quality improvement (a part of the value incentive program) to verify adverse events in fiscal year 2018. Adverse events were identified using patient safety indicator (PSI) clusters and a present on admission indicator (POA). The PSIs consisted of 19 clusters representing subcategories of adverse events, such as hospital-acquired infection. Among the adverse events identified using PSI clusters, "POA = N," which means not present at the time of admission, was only deemed as the case in the final stage. We compared the agreement on the occurrence of adverse events from claims data with a reference standard data set (i.e., checklist of healthcare quality improvement) and presented them by PSI cluster and institution.

RESULTS

The cases of global PSI for any adverse event numbered 27,320 (2.32%) among all diagnostic codes in 2018. In terms of institutional distribution, considerable variation was observed throughout the clusters. For example, only 13.2% of institutions (n = 387) reported any global PSI for any adverse event throughout the whole year. The agreement between the reference standard and the claims data was poor, in the range of 2.2% to 10.8%, in 3 types of adverse events. The current claims data system (i.e., diagnostic codes coupled to POA indicators) failed to capture a large majority of adverse events identified using the reference standard.

CONCLUSIONS

Our results imply that the coding status of International Classification of Diseases, Tenth Revision, codes and POA indicators should be refined before using them as quality indicators.

Severe Maternal Morbidity in California Hospitals: Performance Based on a Validated Multivariable Prediction Model

BACKGROUND

Severe maternal morbidity (SMM) is under development as a quality indicator for maternal health care. The aim of this study is to evaluate California hospital performance based on a standardized SMM measure.

METHODS

California maternal hospital delivery discharge data from 2016 to 2017 were used to develop logistic regression models for SMM, adjusted for clinical risk factors at admission. Data from 2018 were used to test the models and evaluate hospital performance. SMM was defined per the Centers for Disease Control and Prevention, including (excluding) blood transfusion. Independent models were developed for each hospital type: community, teaching, integrated delivery system (IDS), and IDS teaching. Within each type, model-based expected SMM values and observed-to-expected (O/E) ratios were calculated for each hospital. For each hospital type, hospitals were ranked by O/E ratio, and over- and underperforming hospitals were identified using 95% confidence intervals.

RESULTS

Rates of SMM including (excluding) transfusion by hospital type were 1.7% (0.9%) for community, 2.7% (1.5%) for teaching, 2.3% (1.2%) for IDS, and 3.0% (1.6%) for IDS teaching hospitals. In higher-volume community hospitals (≥ 500 births/year), the proportion of underperformers including (excluding) transfusion was 20.7% (11.0%). Summing over all hospital types, 25.3% (14.9%) of hospitals were identified as underperformers in that they experienced significantly more SMM events than expected including (excluding) transfusion.

CONCLUSION

California hospital discharge data demonstrated significant hospital variation in standardized childbirth SMM. These data suggest that a standardized SMM measure may help guide and monitor statewide quality improvement efforts.

An evaluation of an electronic audit and feedback system for patient safety in a tertiary hospital setting: A study protocol

An electronic audit and feedback (e-A&F) system was developed to support healthcare providers' awareness of their own performance, improve delivery of care and ultimately the safety of patients while in hospital. The point-of-care e-A&F system provides healthcare providers, from a 600-bed tertiary hospital in Western Australia, with near real-time feedback via web-based dashboards. The aim of this evaluation is to determine the implications of e-A&F across multiple dimensions and domains of care in a tertiary hospital setting. The study also aims to address the paucity in the literature by validating hypothesised design and implementation mechanisms on its effectiveness. Key datasets to be examined include those related to patient outcomes, staff behaviour and costs. Quantitative methods, such as interrupted time series analysis and multiple logistic regression analysis, amongst other methods, will be employed to achieve these aims.

Incorporating Present-on-Admission Indicators in Medicare Claims to Inform Hospital Quality Measure Risk Adjustment Models

Importance

Present-on-admission (POA) indicators in administrative claims data allow researchers to distinguish between preexisting conditions and those acquired during a hospital stay. The impact of adding POA information to claims-based measures of hospital quality has not yet been investigated to better understand patient underlying risk factors in the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision setting.

Objective

To assess POA indicator use on Medicare claims and to assess the hospital- and patient-level outcomes associated with incorporating POA indicators in identifying risk factors for publicly reported outcome measures used by the Centers for Medicare & Medicaid Services (CMS).

Design, Setting, and Participants

This comparative effectiveness study used national CMS claims data between July 1, 2015, and June 30, 2018. Six hospital quality measures assessing readmission and mortality outcomes were modified to include POA indicators in risk adjustment models. The models using POA were then compared with models using the existing complications-of-care algorithm to evaluate changes in risk model performance. Patient claims data were included for all Medicare fee-for-service and Veterans Administration beneficiaries aged 65 years or older with inpatient hospitalizations for acute myocardial infarction, heart failure, or pneumonia within the measurement period. Data were analyzed between September 2019 and March 2020.

Main Outcomes and Measures

Changes in patient-level (C statistics) and hospital-level (quintile shifts in risk-standardized outcome rates) model performance after including POA indicators in risk adjustment.

Results

Data from a total of 6 027 988 index admissions were included for analysis, ranging from 491 366 admissions (269 209 [54.8%] men; mean [SD] age, 78.2 [8.3] years) for the acute myocardial infarction mortality outcome measure to 1 395 870 admissions (677 158 [48.5%] men; mean [SD] age, 80.3 [8.7] years) for the pneumonia readmission measure. Use of POA indicators was associated with improvements in risk adjustment model performance, particularly for mortality measures (eg, the C statistic increased from 0.728 [95% CI, 0.726-0.730] to 0.774 [95% CI, 0.773-0.776] when incorporating POA indicators into the acute myocardial infarction mortality measure).

Conclusions and Relevance

The findings of this quality improvement study suggest that leveraging POA indicators in the risk adjustment methodology for hospital quality outcome measures may help to more fully capture patients' risk factors and improve overall model performance. Incorporating POA indicators does not require extra effort on the part of hospitals and would be easy to implement in publicly reported quality outcome measures.

The Australian Injury Comorbidity Indices (AICIs) to predict in-hospital complications: A population-based data linkage study

Background

Hospital-admitted patients are at risk of experiencing certain adverse outcomes during their hospital-stay. Patients may need to be admitted to the intensive care unit or be placed on the ventilator while there is also a possibility for complications to develop. Pre-existing comorbidity could increase the risk of these outcomes. The Charlson Comorbidity Index (CCI) and the Elixhauser Comorbidity Measure (ECM), originally derived for mortality outcomes among general medical populations, are widely used for assessing these in-hospital complications even among specific injury populations. This study derived indices to specifically capture the effect of comorbidity on intensive care unit and ventilator use as well as hospital-acquired complications for injury patients.

Methods

Retrospective data on injury hospital-admissions from July 2012 to June 2014 (161,334 patients) for the state of Victoria, Australia was analysed. Results from multivariable regression analysis were used to derive the Australian Injury Comorbidity Indices (AICIs) for intensive care unit and ventilator hours and hospital-acquired complications. The AICIs, CCI and ECM were validated on data from Victoria and two other Australian states.

Results

Five comorbidities were significantly associated with intensive care unit hours, two with ventilator hours and fifteen with hospital-acquired complications for hospitalised injury patients. Not all diseases listed in the CCI or ECM were found to be associated with these outcomes. The AICIs performed equally well in terms of predictive ability to the long-listed ECM and in most instances outperformed the CCI.

Conclusions

Associations between outcomes and comorbidities vary based on the type of outcome measure. The new comorbidity indices developed in this study provide a relevant, parsimonious and up-to-date method to capture the effect of comorbidity on in-hospital complications among admitted injury patients and is better suited for use in that context compared to the CCI and ECM.

Complications, burden and in-hospital death among hospital treated injury patients in Victoria, Australia: a data linkage study

Background

A wide range of outcome measures can be calculated for hospital-treated injury patients. These include mortality, use of critical care services, complications, length of stay, treatment costs, readmission and nursing care after discharge. Each address different aspects and phases of injury recovery and can yield vastly different results. This study aims to: (1) measure and report this range of outcomes in hospital-treated injury patients in a defined population; and (2) describe the associations between injury characteristics, socio-demographics and comorbidities and the various outcomes.

Methods

A retrospective analysis was conducted of injury-related hospital admissions from July 2012 to June 2014 (152,835 patients) in Victoria, Australia. The admission records were linked within the dataset, enabling follow-up, to assess the outcomes of in-hospital death, burden, complications and 30-day readmissions. Associations between factors and outcomes were determined using univariate regression analysis.

Results

The proportion of patients who died in hospital was 0.9%, while 26.8% needed post-discharge care. On average patients had 2.4 complications (confidence interval (CI) 2.4–2.5) related to their initial injury, the mean cost of treating a patient was Australian dollars 7013 (CI 6929–7096) and the median length of stay was one day (inter quartile range 1–3). Intensive-care-unit-stay was recorded in 3% of the patients. All-cause 30-day readmissions occurred in 12.3%, non-planned 30-day readmissions in 7.9%, while potentially avoidable 30-day readmissions were observed in 3.2% of the patients. Increasing age was associated with all outcomes. The need for care post-discharge from hospital was highest among children and the oldest age group (85 years and over). Injury severity was associated with all adverse outcomes. Increasing number of comorbidities increased the likelihood of all outcomes. Overall, outcomes are shown to differ by age, gender, comorbidities, body region injured, injury type and injury severity, and to a lesser extent by socio-economic areas.

Conclusions

Outcomes and risk factors differ depending on the outcome measured, and the method used for measuring the outcome. Similar outcomes measured in different ways produces varying results. Data linkage has provided a valuable platform for a comprehensive overview of outcomes, which can help design and target secondary and tertiary preventive measures.

Electronic supplementary material

The online version of this article (10.1186/s12889-019-7080-y) contains supplementary material, which is available to authorized users.

Diagnostic value and reliability of the present-on-admission indicator in different diagnosis groups: pilot study at a Swiss tertiary care center

Background

With few exceptions the International Statistical Classification of Diseases (ICD) codes for diagnoses and official coding guidelines do not distinguish pre-existing conditions from complications or comorbidities which occur during hospitalization. However, information on diagnosis timing is relevant with regard to the case’s severity, resource consumption and quality of care. In this study we analyzed the diagnostic value and reliability of the present-on-admission (POA) indicator using routinely collected health data.

Methods

We included all inpatient cases of the department of medicine during 2016 with a diagnosis of deep vein thrombosis, decubitus ulcer or delirium. Swiss coding guidelines of 2016 and the definitions of the Swiss medical statistics of hospitals were analyzed to evaluate the potential to encode information on diagnosis timing. The diagnoses were revised by applying the information present-on-admission by a coding specialist and by a medical expert, serving as Gold Standard. The diagnostic value and reliability were evaluated.

Results

The inter-rater reliability for POA of all diagnoses was 0.7133 (Cohen’s kappa), but differed between diagnosis groups (0.558–0.7164). The rate of POA positive of the total applied by the coding specialist versus the expert was similar, but differed between diagnoses. In group “thrombosis” SEN was 0.95, SPE 0.75, PPV 0.97 and NPV 0.60, in group “decubitus ulcer” SEN 0.89, SPE 0.82, PPV 0.89 and NPV 0.82, in group “delirium” SEN 0.91, SPE 0.65, PPV 0.71 and NPV 0.88 For all diagnoses SEN 0.92, SPE 0.73, PPV 0.87, NPV 0.82, summing up the cases of all diagnosis groups.

Conclusions

Coding the POA indicator identified diagnoses which were pre-existent with insufficient reliability on individual patient’s level. The overall fair to sufficient diagnostic quality is appropriate for screening and benchmarking performance on population level. As the medical statistics of hospitals carries no variable on pre-existing conditions, the novel approach to apply the POA indicator to diagnoses gives more information on quality of hospital care and complexity of cases. By preparing documentation for POA reporting diagnostic quality must be increased before implementation for risk-assessment or reimbursement on the individual patient’s level.

Electronic supplementary material

The online version of this article (10.1186/s12913-018-3858-3) contains supplementary material, which is available to authorized users.

Incidence of in-hospital and post-discharge diagnosed hospital-associated venous thromboembolism using linked administrative data

BACKGROUND

Hospital-associated venous thromboembolism (HA-VTE) is a serious adverse event, preventable with appropriate care during and post-admission. Accurate measurement of in-hospital and post-discharge incidences is essential for implementation and evaluation of prevention strategies and monitoring.

AIMS

To estimate in-hospital and post-discharge diagnosed VTE, trends and risk factors.

METHODS

This was a population-based study in New South Wales, Australia, using linked hospital admission and emergency department data for 2010-2013 of adult patients with a minimum stay of 48 h. HA-VTE were diagnosed in-hospital or post-discharge (within 90 days). Multi-level modelling schemes produced adjusted rates and ratios for patient, admission and hospital-related characteristics.

RESULTS

From 1 865 059 admissions, the HA-VTE incidence rate was 9.7 per 1000 admissions; 71% were diagnosed post-discharge, and 4.3% died with a greater risk for VTE diagnosed in hospital compared to post-discharge (8.4% vs 2.6%, P < 0.001). Compared with surgical patients, medical patients developed fewer HA-VTE (IRR = 0.60, 95% CI: 0.58-0.63) but were more likely to be diagnosed post-discharge (OR = 2.19; 95% CI: 2.00-2.40). HA-VTE increased 6.5% over the period, driven by the 44% increase in in-hospital diagnoses and not by the 9% decrease in post-discharge diagnoses.

CONCLUSIONS

HA-VTE is a continuing burden, and diagnosis after recent hospital discharge is notably high. Incidence varies across patients and facilities, highlighting the need for individual VTE risk assessment. Inclusive measures and routine monitoring of HA-VTE incidence and mortality are essential for implementing best practice and assessing effectiveness of prevention strategies.

Accuracy of surgical complication rate estimation using ICD-10 codes

Background

The ICD‐10 codes are used globally for comparison of diagnoses and complications, and are an important tool for the development of patient safety, healthcare policies and the health economy. The aim of this study was to investigate the accuracy of verified complication rates in surgical admissions identified by ICD‐10 codes and to validate these estimates against complications identified using the established Global Trigger Tool (GTT) methodology.

Methods

This was a prospective observational study of a sample of surgical admissions in two Norwegian hospitals. Complications were identified and classified by two expert GTT teams who reviewed patients' medical records. Three trained reviewers verified ICD‐10 codes indicating a complication present on admission or emerging in hospital.

Results

A total of 700 admissions were drawn randomly from 12 966 procedures. Some 519 possible complications were identified in 332 of 700 admissions (47·4 per cent) from ICD‐10 codes. Verification of the ICD‐10 codes against information from patients' medical records confirmed 298 as in‐hospital complications in 141 of 700 admissions (20·1 per cent). Using GTT methodology, 331 complications were found in 212 of 700 admissions (30·3 per cent). Agreement between the two methods reached 83·3 per cent after verification of ICD‐10 codes. The odds ratio for identifying complications using the GTT increased from 5·85 (95 per cent c.i. 4·06 to 8·44) to 25·38 (15·41 to 41·79) when ICD‐10 complication codes were verified against patients' medical records.

Conclusion

Verified ICD‐10 codes strengthen the accuracy of complication rates. Use of non‐verified complication codes from administrative systems significantly overestimates in‐hospital surgical complication rates.

Code correctly

Capturing diagnosis-timing in ICD-coded hospital data: recommendations from the WHO ICD-11 topic advisory group on quality and safety

PURPOSE

To develop a consensus opinion regarding capturing diagnosis-timing in coded hospital data.

METHODS

As part of the World Health Organization International Classification of Diseases-11th Revision initiative, the Quality and Safety Topic Advisory Group is charged with enhancing the capture of quality and patient safety information in morbidity data sets. One such feature is a diagnosis-timing flag. The Group has undertaken a narrative literature review, scanned national experiences focusing on countries currently using timing flags, and held a series of meetings to derive formal recommendations regarding diagnosis-timing reporting.

RESULTS

The completeness of diagnosis-timing reporting continues to improve with experience and use; studies indicate that it enhances risk-adjustment and may have a substantial impact on hospital performance estimates, especially for conditions/procedures that involve acutely ill patients. However, studies suggest that its reliability varies, is better for surgical than medical patients (kappa in hip fracture patients of 0.7-1.0 versus kappa in pneumonia of 0.2-0.6) and is dependent on coder training and setting. It may allow simpler and more precise specification of quality indicators.

CONCLUSIONS

As the evidence indicates that a diagnosis-timing flag improves the ability of routinely collected, coded hospital data to support outcomes research and the development of quality and safety indicators, the Group recommends that a classification of 'arising after admission' (yes/no), with permitted designations of 'unknown or clinically undetermined', will facilitate coding while providing flexibility when there is uncertainty. Clear coding standards and guidelines with ongoing coder education will be necessary to ensure reliability of the diagnosis-timing flag.

Marginal costs of hospital-acquired conditions: information for priority-setting for patient safety programmes and research

OBJECTIVE

To estimate the relative inpatient costs of hospital-acquired conditions.

METHODS

Patient level costs were estimated using computerized costing systems that log individual utilization of inpatient services and apply sophisticated cost estimates from the hospital's general ledger. Occurrence of hospital-acquired conditions was identified using an Australian 'condition-onset' flag for diagnoses not present on admission. These were grouped to yield a comprehensive set of 144 categories of hospital-acquired conditions to summarize data coded with ICD-10. Standard linear regression techniques were used to identify the independent contribution of hospital-acquired conditions to costs, taking into account the case-mix of a sample of acute inpatients (n = 1,699,997) treated in Australian public hospitals in Victoria (2005/06) and Queensland (2006/07).

RESULTS

The most costly types of complications were post-procedure endocrine/metabolic disorders, adding AU$21,827 to the cost of an episode, followed by MRSA (AU$19,881) and enterocolitis due to Clostridium difficile (AU$19,743). Aggregate costs to the system, however, were highest for septicaemia (AU$41.4 million), complications of cardiac and vascular implants other than septicaemia (AU$28.7 million), acute lower respiratory infections, including influenza and pneumonia (AU$27.8 million) and UTI (AU$24.7 million). Hospital-acquired complications are estimated to add 17.3% to treatment costs in this sample.

CONCLUSIONS

Patient safety efforts frequently focus on dramatic but rare complications with very serious patient harm. Previous studies of the costs of adverse events have provided information on 'indicators' of safety problems rather than the full range of hospital-acquired conditions. Adding a cost dimension to priority-setting could result in changes to the focus of patient safety programmes and research. Financial information should be combined with information on patient outcomes to allow for cost-utility evaluation of future interventions.

Temozolomide treatment for aggressive pituitary tumors: correlation of clinical outcome with O(6)-methylguanine methyltransferase (MGMT) promoter methylation and expression

CONTEXT

The typically indolent behavior of pituitary tumors is juxtaposed with high rates of tumor cell invasion into adjacent dural structures, and occasional aggressive behavior. Although clinically significant invasion and malignant transformation remain uncommon, there are limited treatment options available for the management of these aggressive tumors. Recently, case reports have described efficacy of temozolomide for the treatment of aggressive pituitary tumors.

DESIGN

Seven patients with aggressive pituitary tumors have been treated with temozolomide. We compared O(6)-methylguanine methyltransferase (MGMT) promoter methylation and MGMT expression in 14 surgical specimens from these seven patients and correlated these molecular features with the clinical response to temozolomide.

RESULTS

Significant tumor regression was seen in two patients (29%), a 20% reduction in tumor volume with subsequent stable tumor size was noted in one patient, arrest of tumor growth occurred in three patients, and progressive metastatic disease developed during treatment in one patient. The DNA promoter site for MGMT was unmethylated in all 14 adequate specimens, and variable MGMT expression was seen in all 14 cases. There was no correlation between MGMT expression and clinical outcomes.

CONCLUSIONS

We conclude that medical therapy with temozolomide can be helpful in the management of life-threatening pituitary tumors that have failed to respond to conventional treatments. The optimal duration of treatment in patients with stabilization or reduction of tumor size has not been established, and long-term follow up studies are needed.