Lian Leng Low

Applicability of a previously validated readmission predictive index in medical patients in Singapore: a retrospective study

BackgroundHospital readmissions are serious and costly events, and readmission rates are considered to be an indicator of quality in health care management. Several models to identify patients at risk of unplanned readmissions have been developed in Western countries, but little is known about their performance in other countries. This paper reports the possible utility of one such model developed in Canada, the LACE index, in patients in a tertiary hospital in Singapore.MethodsWe used administrative data from Singapore General Hospital for patients admitted between 1st January 2006 and 31st December 2010. Data such as demographic and clinical data including disease codes were extracted. The patient cohort was divided into two groups with a LACE index of 10 as the cutoff. Multivariate logistic regression analysis models were used to compare the outcomes between the two groups of patients with adjustment for age, sex, ethnicity, year of discharge, intensive care unit admission, and admission ward class.ResultsOverall, 127 550 patients were eligible for analysis. Patients with a LACE index ≥ 10 had a higher risk of 30-day unplanned readmission after index discharge (odds ratio [OR]: 4.37; 95% confidence interval [CI]: 4.18-4.57). After adjustment, the risk remained significant (OR: 4.88; 95% CI: CI 4.57-5.22). The C-statistic for the adjusted model was 0.70 (P < 0.001). Similar results were shown for 90-day unplanned readmission and emergency visits after the same adjustment.ConclusionThe use of the LACE index may have significant application in identifying medical patients at high risk of readmission and visits to the Emergency Department in Singapore.

Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore

The LACE index (length of stay, acuity of admission, Charlson comorbidity index, CCI, and number of emergency department visits in preceding 6 months) derived in Canada is simple and may have clinical utility in Singapore to predict readmission risk. We compared the performance of the LACE index with a derived model in identifying 30-day readmissions from a population of general medicine patients in Singapore. Additional variables include patient demographics, comorbidities, clinical and laboratory variables during the index admission, and prior healthcare utilization in the preceding year. 5,862 patients were analysed and 572 patients (9.8%) were readmitted in the 30 days following discharge. Age, CCI, count of surgical procedures during index admission, white cell count, serum albumin, and number of emergency department visits in previous 6 months were significantly associated with 30-day readmission risk. The final logistic regression model had fair discriminative ability c-statistic of 0.650 while the LACE index achieved c-statistic of 0.628 in predicting 30-day readmissions. Our derived model has the advantage of being available early in the admission to identify patients at high risk of readmission for interventions. Additional factors predicting readmission risk and machine learning techniques should be considered to improve model performance.

Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity.

Background To reduce readmissions, it may be cost-effective to consider risk stratification, with targeting intervention programs to patients at high risk of readmissions. In this study, we aimed to derive and validate a prediction model including several novel markers of hospitalization severity, and compare the model with the LACE index (Length of stay, Acuity of admission, Charlson comorbidity index, Emergency department visits in past 6 months), an established risk stratification tool. Method This was a retrospective cohort study of all patients ≥ 21 years of age, who were admitted to a tertiary hospital in Singapore from January 1, 2013 through May 31, 2015. Data were extracted from the hospital’s electronic health records. The outcome was defined as unplanned readmissions within 30 days of discharge from the index hospitalization. Candidate predictive variables were broadly grouped into five categories: Patient demographics, social determinants of health, past healthcare utilization, medical comorbidities, and markers of hospitalization severity. Multivariable logistic regression was used to predict the outcome, and receiver operating characteristic analysis was performed to compare our model with the LACE index. Results 74,102 cases were enrolled for analysis. Of these, 11,492 patient cases (15.5%) were readmitted within 30 days of discharge. A total of fifteen predictive variables were strongly associated with the risk of 30-day readmissions, including number of emergency department visits in the past 6 months, Charlson Comorbidity Index, markers of hospitalization severity such as ‘requiring inpatient dialysis during index admission, and ‘treatment with intravenous furosemide 40 milligrams or more’ during index admission. Our predictive model outperformed the LACE index by achieving larger area under the curve values: 0.78 (95% confidence interval [CI]: 0.77–0.79) versus 0.70 (95% CI: 0.69–0.71). Conclusion Several factors are important for the risk of 30-day readmissions, including proxy markers of hospitalization severity.

Housing as a Social Determinant of Health in Singapore and Its Association with Readmission Risk and Increased Utilization of Hospital Services.

Background Residence in public rental housing is an area-level measure of socioeconomic status, but its impact as a social determinant of health in Singapore has not been studied. We therefore aimed to examine the association of public rental housing with readmission risk and increased utilization of hospital services in Singapore. Methods We conducted a retrospective cohort study using retrospective 2014 data from Singapore General Hospital’s electronic health records. Variables known to affect readmission risk and health-care utilization were identified a priori and include patient demographics, comorbidities, health-care utilization in the preceding 1 year and clinical variables from the index admission in 2014. Multivariate logistic regression was used to evaluate public rental housing as an independent risk factor for admission risk, emergency department (ED), and specialist outpatient clinic attendances. Results A total of 14,457 unique patients were analyzed, and 2,163 patients (15.0%) were rental housing residents. Rental housing patients were significantly more likely to be male; required financial assistance; have chronic obstructive pulmonary disease; usage of anti-depressant and anti-psychotic medications; longer length of hospital stay during the index admission; and higher Charlson Comorbidity Index scores. After adjusting for demographics and clinical variables, staying in public rental housing remained an independent risk factor for readmission within 15 and 30 days, frequent hospital admissions and ED attendances in Singapore. Conclusion Our study showed an association between public rental housing with readmission risk and increased utilization of hospital services in Singapore. A deeper understanding of the residents’ social circumstances and health seeking behavior would be insightful.

Applying the Integrated Practice Unit Concept to a Modified Virtual Ward Model of Care for Patients at Highest Risk of Readmission: A Randomized Controlled Trial

Background Emerging evidence from the virtual ward care model showed that multidisciplinary case management are inadequate to reduce readmissions or death for high risk patients. There is consensus that interventions should encompass both pre-hospital discharge and post-discharge transitional care to be effective. Integrated practice units (IPU) had been proposed as an approach of restructuring the organization and work processes of multidisciplinary teams to achieve value in healthcare. Our primary objective is to evaluate if the novel application of the IPU concept to organize a modified virtual ward model incorporating pre-hospital discharge transitional care can reduce readmissions of patients at highest risk for readmission. Methods We conducted an open label, assessor blinded randomized controlled trial on patients with one or more unscheduled readmissions in the prior 90 days and LACE score ≥ 10. 840 patients were randomized in 1:1 ratio and blocks of 6 to the intervention program (n = 420) or control (n = 420). Allocation concealment was effected via an off-site telephone service maintained by a hospital administrator. Intervention patients received discharge planning, medication reconciliation, coaching on self-management of chronic diseases using standardized action plans and an individualized care plan complete with written discharge instructions, appointments schedule, medication changes and the contact information of the outpatient VW nurse before discharge. At discharge, care is handed over to the outpatient VW team. Patients were closely monitored in the VW for three months that included a telephone review within 72 hours of discharge, home assessment, regular telephone reviews to identify early complications and early review clinics for patients who destabilize. The VW meet daily to discuss new patients and review care plans for patients. Control patients received standard hospital care that included a standardized patient copy of the hospital discharge summary listing their medical diagnoses and medications; and follow up is arranged with a primary care provider or specialist as considered necessary. The primary outcome was the unplanned readmission rate to any hospital within 30 days of discharge. Secondary outcomes included the unplanned readmission rate, emergency department (ED) attendance rate to any hospital and the probability without readmission or death up to 180 days of discharge. Length of stay and mortality rate at 90-day were compared between the two groups. Outcome data were objectively retrieved from the hospital and National Electronic Health Records by a blinded outcome assessor. Findings All patients’ outcomes were included in an intention-to-treat analysis. The characteristics of both study groups were similar. Patients in the intervention group had a significant reduction in the number of 30-day readmissions, IRR 0.67 (95% CI, 0.52 to 0.86, p = 0.001) and the number of 30-day emergency department attendances, IRR 0.60 (95% CI, 0.46 to 0.79, p<0.001) compared to those receiving standard hospital care. The effectiveness was sustained at 90 and 180 days. The intervention group utilized 1164 fewer hospital bed days at 90-day post discharge. No adverse events were reported. Conclusion Applying the integrated practice unit concept to the virtual ward program resulted in reduced readmissions in patients who are at highest risk of readmission.

Predicting frequent hospital admission risk in Singapore: a retrospective cohort study to investigate the impact of comorbidities, acute illness burden and social determinants of health

Objectives To evaluate the impact of comorbidities, acute illness burden and social determinants of health on predicting the risk of frequent hospital admissions. Design Multivariable logistic regression was used to associate the predictive variables extracted from electronic health records and frequent hospital admission risk. The models performance of our predictive model was evaluated using a 10-fold cross-validation. Setting A single tertiary hospital in Singapore. Participants All adult patients admitted to the hospital between 1 January 2013 and 31 May 2014 (n=25 244). Main outcome measure Frequent hospital admissions, defined as 3 or more inpatient admissions within 12 months of discharge. Area under the receiver operating characteristic curve (AUC) of the predictive model, and the sensitivity, specificity and positive predictive values for various cut-offs. Results 4322 patients (17.1%) met the primary outcome. 11 variables were observed as significant predictors and included in the final regression model. The strongest independent predictor was treatment with antidepressants in the past 1 year (adjusted OR 2.51, 95% CI 2.26 to 2.78). Other notable predictors include requiring dialysis and treatment with intravenous furosemide during the index admission. The predictive model achieved an AUC of 0.84 (95% CI 0.83 to 0.85) for predicting frequent hospital admission risk, with a sensitivity of 73.9% (95% CI 72.6% to 75.2%), specificity of 79.1% (78.5% to 79.6%) and positive predictive value of 42.2% (95% CI 41.1% to 43.3%) at the cut-off of 0.235. Conclusions We have identified several predictors for assessing the risk of frequent hospital admissions that achieved high discriminative model performance. Further research is necessary using an external validation cohort.

Oral Vitamin B12 Replacement for the Treatment of Pernicious Anemia.

Many patients with pernicious anemia are treated with lifelong intramuscular (IM) vitamin B12 replacement. As early as the 1950s, there were studies suggesting that oral vitamin B12 replacement may provide adequate absorption. Nevertheless, oral vitamin B12 replacement in patients with pernicious anemia remains uncommon in clinical practice. The objective of this review is to provide an update on the effectiveness of oral vitamin B12 for the treatment of pernicious anemia, the recommended dosage, and the required frequency of laboratory test and clinical monitoring. Relevant articles were identified by PubMed search from January 1, 1980 to March 31, 2016 and through hand search of relevant reference articles. Two randomized controlled trials, three prospective papers, one systematic review, and three clinical reviews fulfilled our inclusion criteria. We found that oral vitamin B12 replacement at 1000 μg daily was adequate to replace vitamin B12 levels in patients with pernicious anemia. We conclude that oral vitamin B12 is an effective alternative to vitamin B12 IM injections. Patients should be offered this alternative after an informed discussion on the advantages and disadvantages of both treatment options.

Pressure Ulcer Risk Assessment and Prevention for the Family Physician

Pressure ulcers are common and result in serious medical complications, prolonged hospital stay and frequent readmissions. With a rapidly ageing population and increasing chronic disease burden in Singapore, the prevalence of pressure ulcers will increase further. Family physicians will encounter more pressure ulcers in their practices in the primary, intermediate and long term care settings. We conducted a comprehensive literature review on established evidence on pressure ulcer risk assessment and prevention, and also reviewed current hospital protocols in Singapore. We found that many studies on pressure ulcer risk assessment and prevention lacked methodological quality to provide robust evidence and conclusions. Consequently, many of the recommendations in major international guidelines and protocols of major hospitals in Singapore are based on a combination of best available evidence, best practices and consensus opinion. We provided a summary of key recommendations for family physicians, based on the Strength of Recommendation Taxonomy (SORT) framework. We also hope to stimulate interest in regular updates of local guidelines and major hospital protocols in Singapore to reflect the latest evidence based strategies on risk assessment and prevention of pressure ulcers.

A systematic review of the barriers affecting medication adherence in patients with rheumatic diseases

Medication adherence is a crucial part in the management of rheumatic diseases, especially with many such patients requiring long-term medications. In this paper, we aim to systematically review the literature for the factors associated with medication adherence in the rheumatic patient population. We carried out a systematic literature search using PubMed®, PsychInfo® and Embase ® with relevant keywords and employed the PRISMA® criteria. We included English peer-reviewed articles that studied the factors affecting medication adherence in patients with rheumatic diseases, which were assessed by two independent reviewers. Hand searches were conducted and relevant factors were extracted and classified using the World Health Organization (WHO)’s five dimensions of medication adherence. A simple diagram was drawn to summarise the factors extracted. 1977 articles were identified and reviewed and 90 articles were found to be relevant. A total of 17 factors and 38 sub-factors were identified and categorized based on the WHO’s five dimensions of medication adherence. A hand model for medication adherence was developed to succinctly summarise these dimension to remind clinicians the importance of medication adherence in daily practice. We conducted a systematic review on the various factors including patient, therapy, condition, health system and socioeconomic-related factors that affected medication adherence in rheumatic patients. We found 17 factors and 38 sub-factors that affected medication adherence in this population. This systematic review can facilitate future focused research in unexplored dimensions.

Performance of the LACE index to identify elderly patients at high risk for hospital readmission in Singapore

Abstract Unplanned readmissions may be avoided by accurate risk prediction and appropriate resources could be allocated to high risk patients. The Length of stay, Acuity of admission, Charlson comorbidity index, Emergency department visits in past six months (LACE) index was developed to predict hospital readmissions in Canada. In this study, we assessed the performance of the LACE index in a Singaporean cohort by identifying elderly patients at high risk of 30-day readmissions. We further investigated the use of additional risk factors in improving readmission prediction performance. Data were extracted from the hospitals electronic health records (EHR) for all elderly patients ≥ 65 years, with alive-discharge episodes from Singapore General Hospital in 2014. In addition to LACE, we also collected patients’ data during the index admission, including demographics, medical history, laboratory results, and previous medical utilization. Among the 17,006 patients analyzed, 2051 or 12.1% of them were observed 30-day readmissions. The final predictive model was better than the LACE index in terms of discriminative ability; c-statistic of LACE index and final logistic regression model was 0.595 and 0.628, respectively. The LACE index had poor discriminative ability in identifying elderly patients at high risk of 30-day readmission, even if it was augmented with additional risk factors. Further studies should be conducted to discover additional factors that may enable more accurate and timely identification of patients at elevated risk of readmissions, so that necessary preventive actions can be taken.