Verification of administrative data to measure palliative care at terminal hospital stays.

BACKGROUND: Administrative data and clinician documentation have not been directly compared for reporting palliative care, despite concerns about under-reporting. OBJECTIVE: The aim of this study was to verify the use of routinely collected administrative data for reporting in-hospital palliation and to examine factors associated with coded palliative care in hospital administrative data. METHOD: Hospital administrative data and inpatient palliative care activity documented in medical records were compared for patients dying in hospital between 1 July 2017 and 31 December 2017. Coding of palliative care in administrative data is based on hospital care type coded as "palliative care" and/or assignment of the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) palliative care diagnosis code Z51.5. Medical records were searched for specified keywords, which, read in context, indicated a palliative approach to care. The list of keywords (palliative, end of life, comfort care, cease observations, crisis medications, comfort medications, syringe driver, pain or symptom management, no cardiopulmonary resuscitation, advance medical plan/resuscitation plan, deteriorating, agitation, restless and delirium) was developed in consultation with seven local clinicians specialising in palliative care or geriatric medicine. RESULTS: Of the 576 patients who died in hospital, 246 were coded as having received palliative care, either solely by the ICD-10-AM diagnosis code Z51.5 (42%) or in combination with a "palliative care" care type (58%). Just over one-third of dying patients had a palliative care specialist involved in their hospital care. Involvement of a palliative care specialist and a cancer diagnosis substantially increased the odds of a Z51.5 code (odds ratio = 11 and 4, respectively). The majority of patients with a "syringe driver" or identified as being at the "end of life" were assigned a Z51.5 code (73.5% and 70.5%, respectively), compared to 53.8% and 54.7%, respectively, for "palliative" or "comfort care." For each keyword indicating a palliative approach to care, the Z51.5 code was more likely to be assigned if the patient had specialist palliative care input or if they had cancer. CONCLUSION: Our results suggest administrative data under-represented in-hospital palliative care, at least partly due to medical record documentation that failed to meet ICD-10-AM coding criteria. Collaboration between clinicians and coders can enhance the quality of records and, consequently, administrative data.

Inequalities in end-of-life palliative care by country of birth in New South Wales, Australia: a cohort study.

Objective This study investigated variation in in-hospital palliative care according to the decedent's country of birth. Methods A retrospective cohort study was performed of 73469 patients who died in a New South Wales public hospital between July 2010 and June 2015 and were diagnosed with a palliative care-amenable condition. Differences in receipt of palliative care by country of birth were examined using multilevel logistic regression models adjusted for confounding. Results In this cohort, 26444 decedents received palliative care during their last hospital stay. In the adjusted analysis, 40% rate differences (median odds ratio 1.39; 95% confidence interval 1.31-1.51) were observed in receipt of palliative care between country of birth groups. Conclusions There are differences in in-hospital palliative care at the end of life between population groups born in different countries living in Australia. The implementation of culturally sensitive palliative care programs may help reduce these inequalities. Further studies are needed to identify the determinants of the differences observed in this study and to investigate whether these differences persist in the community setting. What is known about the topic? International studies have reported inequities in access to palliative care between ethnic groups. What does this paper add? We observed differences in in-hospital palliative care between decedents from different countries of birth in New South Wales, Australia. These differences remained after adjusting for individual, area and hospital characteristics. What are the implications for practitioners? Implementation of culturally sensitive palliative care services and targeting groups with low rates of palliative care can reduce these inequalities and improve a patient's quality of life.

Specialist Palliative Care Activity at an Acute Care Tertiary Hospital and Its Representation in Administrative Data.

OBJECTIVE: To quantify and examine specialist palliative care (SPC) in-hospital activity and compare it to routinely collected administrative data on palliative care (PC). METHODS: All patients discharged from a large acute care tertiary hospital in New South Wales, Australia, between July 1 and December 31, 2017, were identified from the hospital's data warehouse. Administrative data were supplemented with information from the electronic medical record for hospital stays which were assigned the PC additional diagnosis code (Z51.5); had a "palliative care" care type; or included SPC consultation. RESULTS: Of 34 653 hospital stays, 524 were coded as receiving PC-based on care type (43%) and/or diagnosis code Z51.5 (100%). Specialist palliative care provided 1717 consultations over 507 hospital stays. Patients had 2 (median; interquartile range: 1-4) consultations during an average stay of 15.3 days (SD 15.78; median 10); the first occurred 7.0 days (SD 12.13; median 3) after admission. Of patient stays with an SPC consultation, 70% were assigned the PC Z51.5 code; 60% were referred for symptom management; 68% had cancer. One hundred forty-one patients were under a palliative specialist-either from initial hospital admission (49.6%) or later in their stay. CONCLUSIONS: Palliative care specialists provide expert input into patient management, benefitting patients and other clinicians. Administrative data inadequately capture their involvement in patient care, especially consultations, and are therefore inappropriate for reporting SPC activity. Exclusion of information related to SPC activity results in an incomplete and distorted representation of PC services and fails to acknowledge the valuable contribution made by SPC.

Variations in hospital inpatient palliative care service use: a retrospective cohort study.

OBJECTIVE: Use of palliative care in hospitals for people at end of life varies. We examined rate and time of in-hospital palliative care use and associated interhospital variations. METHODS: We used admissions from all hospitals in New South Wales, Australia, within a 12-month period, for a cohort of adults who died in 73 public acute care hospitals between July 2010 and June 2014. Receiving palliative care and its timing were based on recorded use. RESULTS: Among 90 696 adults who died, 27% received palliative care, and the care was initiated 7.6 days (mean; SD: 3.3 days) before death. Over the 5-year period, the palliative care rate rose by 58%, varying extent across chronic conditions. The duration of palliative care before death declined by 7%. Patient (demographics, morbidities and service use) and hospital factors (size, location and availability of palliative care unit) explained half of the interhospital variation in outcomes: adjusted IQR in rate and duration of palliative care among hospitals were 23%-39% and 5.2-8.7 days, respectively. Hospitals with higher rates often initiated palliative care earlier (correlation: 0.39; p<0.01). CONCLUSION: Despite an increase over time in the palliative care rate, its initiation was late and of brief duration. Palliative care use was associated with patient and hospital characteristics; however, half of the between hospital variation remained unexplained. The observed suboptimal practices and variability indicate the need for expanded and standardised use of palliative care supported by assessment tools, service enhancement and protocols.

Inpatient palliative care of people dying in New South Wales hospitals or soon after discharge.

BACKGROUND: Palliative care can benefit all patients with life-limiting diseases. AIM: To describe hospital use in the final year of life, timing of palliative care and variations by age and disease for patients receiving inpatient palliative care. METHODS: Retrospective cohort study of all New South Wales residents aged 50 years and older who died (decedents) between July 2010 and June 2015 in hospital or within 30 days of discharge. Care type and diagnosis codes identified decedents who received inpatient palliative care. RESULTS: Of 150 770 decedents, 34.4% received palliative care a median of 10 days before death. Decedents were more likely to receive palliative care if they had cancer (64.7% vs 13.3% for those without chronic conditions) or were younger (46.3% vs 25.0% of the oldest decedents). In their last year of life, palliated decedents, on average, had three emergency department presentations and four hospital admissions - one involving surgery and one where palliation was the intent of care. Of the 30.1 days spent in hospital, 8.7 days involved palliative care. Older age and non-cancer diagnoses were associated with fewer days of inpatient palliation and shorter time between first palliative admission and death. Decedents dying out of hospital started palliative care 18 days earlier than those dying in hospital. CONCLUSION: Most decedents did not receive palliative care during hospital admission, and even then only very late in life, limiting its benefits. Improved recognition of palliative need, including earlier identification regardless of age and disease, will enhance the quality of care for the dying.

Variation in out-of-hospital death among palliative care inpatients across public hospitals in New South Wales, Australia.

BACKGROUND: Place of death is an important indicator in palliative care, as out-of-hospital death is often preferred by patients and is less costly for the healthcare system. AIM: To examine variation and contributing factors in out-of-hospital death after receiving palliative care in hospital to inform improvement in transition of care between hospitals and communities. METHODS: Using hospital linked data (July 2010, June 2015) we followed individuals aged 50 or older who received palliative care in hospital and within 3 months to death who were last admitted to a public acute-care hospital in New South Wales, Australia (73 hospitals). RESULTS: Among 25 359 palliative care inpatients, 3677 (14%) died out of hospital. The out-of-hospital death rate was lower for younger patients, males and those living in the most deprived areas; it was higher for cancer patients and those who received palliative care before their last admission. Hospital size, location and availability of hospice care unit were not influential. Across hospitals, the median crude rate of out-of-hospital death was 14% (interquartile range 10-19%). The contributing factors explained 19% of the variation, resulting in a rate difference of 44% between hospitals with high versus low rates; 25% of hospitals had a higher and 14% had a lower than average adjusted out-of-hospital death rate. CONCLUSION: The majority of patients who received palliative care in hospital stayed in hospital until death. The variation in out-of-hospital death across hospitals was considerable and mostly remained unexplained. This variability warrants investigation into transition of palliative care between hospitals and communities to inform interventions.

Validity of routinely collected data in identifying hip fractures at a major tertiary hospital in Australia.

OBJECTIVES: To examine the validity of routinely collected data in identifying hip fractures (HFs) and to identify factors associated with incorrect coding. METHOD: In a prospective cohort study between January 2014 and June 2016, HFs were identified using physician diagnosis and diagnostic imaging and were recorded in a Registry. Records of HFs in the health information exchange (HIE) were identified using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification/Australian Classification of Health Interventions/Australian Coding Standards codes. New HFs were estimated by episode of care, hospital admission and with an algorithm. Data from the HIE and the Registry were compared. RESULTS: The number of HFs as the principal diagnosis (PD) recorded by episode (864) was higher than by admission (743), by algorithm (711) and in the Registry (638). The sensitivity was high for all methods (90-93%) but the positive predictive value was lower for episode (68%) than for admission (80%) or algorithm (81%). The number of HFs with surgery recorded in the PD by episode (639), algorithm (630) and in the Registry (623) was similar but higher than by admission (589). The episode and algorithm methods also had higher sensitivity (91-92%) than the admission method (84%) for HFs with surgery. Factors associated with coding errors included subsequent HF, long hospital stay, intracapsular fracture, younger age, male, HF without surgery and death in hospital. CONCLUSIONS: When it is not practical to use the algorithm for regular monitoring of HFs, using PD by admission to estimate total HFs and PD by episode in combination with a procedure code to estimate HFs with surgery can produce robust estimations.

Toxicity after post-prostatectomy image-guided intensity-modulated radiotherapy using Australian guidelines.

INTRODUCTION: We evaluated single institution toxicity outcomes after post-prostatectomy radiotherapy (PPRT) via image-guided intensity-modulated radiation therapy (IG-IMRT) with implanted fiducial markers following national eviQ guidelines, for which late toxicity outcomes have not been published. METHODS: Prospectively collected toxicity data were retrospectively reviewed for 293 men who underwent 64-66 Gy IG-IMRT to the prostate bed between 2007 and 2015. RESULTS: Median follow-up after PPRT was 39 months. Baseline grade ≥2 genitourinary (GU), gastrointestinal (GI) and sexual toxicities were 20.5%, 2.7% and 43.7%, respectively, reflecting ongoing toxicity after radical prostatectomy. Incidence of new (compared to baseline) acute grade ≥2 GU and GI toxicity was 5.8% and 10.6%, respectively. New late grade ≥2 GU, GI and sexual toxicity occurred in 19.1%, 4.7% and 20.2%, respectively. However, many patients also experienced improvements in toxicities. For this reason, prevalence of grade ≥2 GU, GI and sexual toxicities 4 years after PPRT was similar to or lower than baseline (21.7%, 2.6% and 17.4%, respectively). There were no grade ≥4 toxicities. CONCLUSIONS: Post-prostatectomy IG-IMRT using Australian contouring guidelines appears to have tolerable acute and late toxicity. The 4-year prevalence of grade ≥2 GU and GI toxicity was virtually unchanged compared to baseline, and sexual toxicity improved over baseline. This should reassure radiation oncologists following these guidelines. Late toxicity rates of surgery and PPRT are higher than following definitive IG-IMRT, and this should be taken into account if patients are considering surgery and likely to require PPRT.

Incidence and Variation of Discrepancies in Recording Chronic Conditions in Australian Hospital Administrative Data.

Diagnostic data routinely collected for hospital admitted patients and used for case-mix adjustment in care provider comparisons and reimbursement are prone to biases. We aim to measure discrepancies, variations and associated factors in recorded chronic morbidities for hospital admitted patients in New South Wales (NSW), Australia. Of all admissions between July 2010 and June 2014 in all NSW public and private acute hospitals, admissions with over 24 hours stay and one or more of the chronic conditions of diabetes, smoking, hepatitis, HIV, and hypertension were included. The incidence of a non-recorded chronic condition in an admission occurring after the first admission with a recorded chronic condition (index admission) was considered as a discrepancy. Poisson models were employed to (i) derive adjusted discrepancy incidence rates (IR) and rate ratios (IRR) accounting for patient, admission, comorbidity and hospital characteristics and (ii) quantify variation in rates among hospitals. The discrepancy incidence rate was highest for hypertension (51% of 262,664 admissions), followed by hepatitis (37% of 12,107), smoking (33% of 548,965), HIV (27% of 1500) and diabetes (19% of 228,687). Adjusted rates for all conditions declined over the four-year period; with the sharpest drop of over 80% for diabetes (47.7% in 2010 vs. 7.3% in 2014), and 20% to 55% for the other conditions. Discrepancies were more common in private hospitals and smaller public hospitals. Inter-hospital differences were responsible for 1% (HIV) to 9.4% (smoking) of variation in adjusted discrepancy incidences, with an increasing trend for diabetes and HIV. Chronic conditions are recorded inconsistently in hospital administrative datasets, and hospitals contribute to the discrepancies. Adjustment for patterns and stratification in risk adjustments; and furthermore longitudinal accumulation of clinical data at patient level, refinement of clinical coding systems and standardisation of comorbidity recording across hospitals would enhance accuracy of datasets and validity of case-mix adjustment.

Use of hypofractionated post-mastectomy radiotherapy reduces health costs by over $2000 per patient: An Australian perspective.

INTRODUCTION: The most recent clinical practice guidelines released by Cancer Australia draw attention to unanswered questions concerning the health economic considerations associated with hypofractionated radiotherapy. This study aimed to quantify and compare the healthcare costs at a regional Australian radiotherapy institute with respect to conventionally fractionated post-mastectomy radiotherapy (Cf-PMRT) versus hypofractionated post-mastectomy radiotherapy (Hf-PMRT) administration. METHODS: Medical records of 196 patients treated with post-mastectomy radiotherapy at the NSW North Coast Cancer Institute from February 2008 to June 2014 were retrospectively reviewed. Australian Medicare item numbers billed for patients receiving either Cf-PMRT of 50 Gy in 25 daily fractions or Hf-PMRT of 40.05 Gy in 15 daily fractions were calculated. Decision tree analysis was used to model costs. Independent-samples t-tests and Mann-Whitney U-tests were used to compare crude average costs for Cf-PMRT and Hf-PMRT and determine which treatment components accounted for any differences. RESULTS: Hf-PMRT, with or without irradiation to the regional lymph nodes, was associated with significantly reduced Medicare costs ($5613 AUD per patient for Hf-PMRT vs $8272 AUD per patient for Cf-PMRT; P < 0.001). Savings associated with Hf-PMRT ranged from $1353 (22.1%) for patients receiving no regional irradiation to $2898 (32.0%) for patients receiving both axillary and supraclavicular therapy. CONCLUSIONS: Hf-PMRT results in a significant reduction in the financial costs associated with treating breast cancer patients in a regional Australian setting when compared with Cf-PMRT.

Electrocardiogram Derived QRS Duration >120 ms is Associated With Elevated Plasma Homocysteine Levels in a Rural Australian Cross-Sectional Population.

Homocysteine levels in the low to moderate range for cardiovascular risk have been previously associated with left ventricular cardiac hypertrophy (LVH). Electrocardiogram (ECG) derived QRS duration has also been used as an epidemiological screening marker for cardiac hypertrophy risk. QRS duration cut offs have not been previously modeled to assess homocysteine levels in community populations. Our aims are to determine if QRS duration is associated with an elevated homocysteine level in a cross-sectional Australian aging rural population.A retrospective study design utilizing a rural health diabetic screening clinic database containing observational data from the period January 9, 2002 till September 25, 2012. One hundred seventy-eight individuals (>21 years of age) from the database were included in the study. Inclusion criteria included being nondiabetic and having both a QRS duration measure and a matching homocysteine level within the same subject. All participants were from the Albury-Wodonga area, with a mean age of >64 years for both sexes.Mean population homocysteine plasma levels were 10.4 µmol/L (SD = 3.6). The mean QRS duration was 101.8 ms (SD = 17.4). Groups were stratified on the basis of QRS duration (≤120 ms [n = 157] and >120 ms [n = 21]). QRS duration subgroup (≤120 ms vs >120 ms) mean differences across homocysteine levels were 10.1 µmol/L (SD = 3.3) and 12.2 µmol/L (SD = 4.7), respectively (P = 0.016). Other ECG parameters (PQ interval, QTc interval, and QT dispersion) measurements were not significantly associated with differences in plasma homocysteine (P = not significant).We conclude that in community populations homocysteine may be moderately elevated when QRS durations are >120 ms. Small additional increases in homocysteine levels may suggest a risk factor for ECG diagnosis of LVH.

Planning magnetic resonance imaging for prostate cancer intensity-modulated radiation therapy: Impact on target volumes, radiotherapy dose and androgen deprivation administration.

AIMS: Magnetic resonance imaging (MRI) scans are increasingly utilized for radiotherapy planning to contour the primary tumors of patients undergoing intensity-modulated radiation therapy (IMRT). These scans may also demonstrate cancer extent and may affect the treatment plan. We assessed the impact of planning MRI detection of extracapsular extension, seminal vesicle invasion, or adjacent organ invasion on the staging, target volume delineation, doses, and hormonal therapy of patients with prostate cancer undergoing IMRT. METHODS: The records of 509 consecutive patients with planning MRI scans being treated with IMRT for prostate cancer between January 2010 and July 2012 were retrospectively reviewed. Tumor staging and treatment plans before and after MRI were compared. RESULTS: Of the 509 patients, 103 (20%) were upstaged and 44 (9%) were migrated to a higher risk category as a result of findings at MRI. In 94 of 509 patients (18%), the MRI findings altered management. Ninety-four of 509 patients (18%) had a change to their clinical target volume (CTV) or treatment technique, and in 41 of 509 patients (8%) the duration of hormone therapy was changed because of MRI findings. CONCLUSION: The use of radiotherapy planning MRI altered CTV design, dose and/or duration of androgen deprivation in 18% of patients in this large, single institution series of men planned for dose-escalated prostate IMRT. This has substantial implications for radiotherapy target volumes and doses, as well as duration of androgen deprivation. Further research is required to investigate whether newer MRI techniques can simultaneously fulfill staging and radiotherapy contouring roles.

Rate of venous thromboembolism among surgical patients in Australian hospitals: a multicentre retrospective cohort study.

OBJECTIVES: Despite the burden of venous thromboembolism (VTE) among surgical patients on health systems in Australia, data on VTE incidence and its variation within Australia are lacking. We aim to explore VTE and subsequent mortality rates, trends and variations across Australian acute public hospitals. SETTING: A large retrospective cohort study using all elective surgical patients in 82 acute public hospitals during 2002-2009 in New South Wales, Australia. PARTICIPANTS: Patients underwent elective surgery within 2 days of admission, aged between 18 and 90 years, and who were not transferred to another acute care facility; 4 362 624 patients were included. OUTCOME MEASURES: VTE incidents were identified by secondary diagnostic codes. Poisson mixed models were used to derive adjusted incidence rates and rate ratios (IRR). RESULTS: 2/1000 patients developed postoperative VTE. VTE increased by 30% (IRR=1.30, CI 1.19 to 1.42) over the study period. Differences in the VTE rates, trends between hospital peer groups and between hospitals with the highest and those with the lowest rates were significant (between-hospital variation). Smaller hospitals, accommodated in two peer groups, had the lowest overall VTE rates (IRR=0.56:0.33 to 0.95; IRR=0.37:0.23 to 0.61) and exhibited a greater increase (64% and 237% vs 19%) overtime and greater between-hospital variations compared to larger hospitals (IRR=8.64:6.23 to 11.98; IRR=8.92:5.49 to 14.49 vs IRR=3.70:3.32 to 4.12). Mortality among patients with postoperative VTE was 8% and remained stable overtime. No differences in post-VTE death rates and trends were seen between hospital groups; however, larger hospitals exhibited less between-hospital variations (IRR=1.78:1.30 to 2.44) compared to small hospitals (IRR>23). Hospitals performed differently in prevention versus treatment of postoperative VTE. CONCLUSIONS: VTE incidence is increasing and there is large variation between-hospital and within-hospital peer groups suggesting a varied compliance with VTE preventative strategies and the potential for targeted interventions and quality improvement opportunities.

Change point estimation in monitoring survival time.

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.

Bayesian change point detection in monitoring cardiac surgery outcomes.

Precise identification of the time when a clinical process has changed, a control chart's signal, enables clinicians to search for a potential special cause more effectively. In this article, we develop a change point estimation method for Bernoulli processes in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point model and Markov Chain Monte Carlo to obtain posterior distributions of the change point parameters. The performance of the Bayesian estimator is investigated through applications on clinical data. We monitor outcomes of cardiac surgery and angioplasty procedures using Bernoulli exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts. We then identify the time of changes in prior signals obtained from charts. Study of the known potential causes of changes in the outcomes reveals that estimated change points and shifts in the known causes are coincident.