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Predicting risk of hospitalisation: a retrospective population-based analysis in a paediatric population in Emilia-Romagna, Italy.

ABSTRACT: OBJECTIVES:Develop predictive models for a paediatric population that provide information for paediatricians and health authorities to identify children at risk of hospitalisation for conditions that may be impacted through improved patient care. DESIGN:Retrospective healthcare utilisation analysis with multivariable logistic regression models. DATA:Demographic information linked with utilisation of health services in the years 2006-2014 was used to predict risk of hospitalisation or death in 2015 using a longitudinal administrative database of 527?458 children aged 1-13 years residing in the Regione Emilia-Romagna (RER), Italy, in 2014. OUTCOME MEASURES:Models designed to predict risk of hospitalisation or death in 2015 for problems that are potentially avoidable were developed and evaluated using the C-statistic, for calibration to assess performance across levels of predicted risk, and in terms of their sensitivity, specificity and positive predictive value. RESULTS:Of the 527?458 children residing in RER in 2014, 6391 children (1.21%) were hospitalised for selected conditions or died in 2015. 49?486 children (9.4%) of the population were classified in the 'At Higher Risk' group using a threshold of predicted risk >2.5%. The observed risk of hospitalisation (5%) for the 'At Higher Risk' group was more than four times higher than the overall population. We observed a C-statistic of 0.78 indicating good model performance. The model was well calibrated across categories of predicted risk. CONCLUSIONS:It is feasible to develop a population-based model using a longitudinal administrative database that identifies the risk of hospitalisation for a paediatric population. The results of this model, along with profiles of children identified as high risk, are being provided to the paediatricians and other healthcare professionals providing care to this population to aid in planning for care management and interventions that may reduce their patients' likelihood of a preventable, high-cost hospitalisation.

SUBMITTER: Louis DZ

PROVIDER: S-EPMC5942467 | biostudies-other | 2018 May

REPOSITORIES: biostudies-other

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Predicting risk of hospitalisation: a retrospective population-based analysis in a paediatric population in Emilia-Romagna, Italy.

Louis Daniel Z DZ Callahan Clara A CA Robeson Mary M Liu Mengdan M McRae Jacquelyn J Gonnella Joseph S JS Lombardi Marco M Maio Vittorio V

BMJ open 20180505 5

<h4>Objectives</h4>Develop predictive models for a paediatric population that provide information for paediatricians and health authorities to identify children at risk of hospitalisation for conditions that may be impacted through improved patient care.<h4>Design</h4>Retrospective healthcare utilisation analysis with multivariable logistic regression models.<h4>Data</h4>Demographic information linked with utilisation of health services in the years 2006-2014 was used to predict risk of hospital ...[more]

PMID: 29730620

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Project description:ObjectivesDevelop predictive models using an administrative healthcare database that provide information for Patient-Centred Medical Homes to proactively identify patients at risk of hospitalisation for conditions that may be impacted through improved patient care.DesignRetrospective healthcare utilisation analysis with multivariate logistic regression models.DataA population-based longitudinal database of residents served by the Emilia-Romagna, Italy, health service in the years 2004-2012 including demographic information and utilisation of health services by 3,726,380 people aged ≥18 years.Outcome measuresModels designed to predict risk of hospitalisation or death in 2012 for problems that are potentially avoidable were developed and evaluated using the area under the receiver operating curve C-statistic, in terms of their sensitivity, specificity and positive predictive value, and for calibration to assess performance across levels of predicted risk.ResultsAmong the 3,726,380 adult residents of Emilia-Romagna at the end of 2011, 449,163 (12.1%) were hospitalised in 2012; 4.2% were hospitalised for the selected conditions or died in 2012 (3.6% hospitalised, 1.3% died). The C-statistic for predicting 2012 outcomes was 0.856. The model was well calibrated across categories of predicted risk. For those patients in the highest predicted risk decile group, the average predicted risk was 23.9% and the actual prevalence of hospitalisation or death was 24.2%.ConclusionsWe have developed a population-based model using a longitudinal administrative database that identifies the risk of hospitalisation for residents of the Emilia-Romagna region with a level of performance as high as, or higher than, similar models. The results of this model, along with profiles of patients identified as high risk are being provided to the physicians and other healthcare professionals associated with the Patient Centred Medical Homes to aid in planning for care management and interventions that may reduce their patients' likelihood of a preventable, high-cost hospitalisation.

Project description:BackgroundBreast cancer is one of the most common cancer in women and a proportion of patients experiences brain metastases with poor prognosis. The study aimed to construct a novel predictive clinical model to evaluate the overall survival (OS) of patients with postoperative brain metastasis of breast cancer (BCBM) and validate its effectiveness.MethodsFrom 2010 to 2020, a total of 310 female patients with BCBM were diagnosed in The Affiliated Cancer Hospital of Xinjiang Medical University, and they were randomly assigned to the training cohort and the validation cohort. Data of another 173 BCBM patients were collected from the Surveillance, Epidemiology, and End Results Program (SEER) database as an external validation cohort. In the training cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used to determine the fundamental clinical predictive indicators and the nomogram was constructed to predict OS. The model capability was assessed using receiver operating characteristic, C-index, and calibration curves. Kaplan-Meier survival analysis was performed to evaluate clinical effectiveness of the risk stratification system in the model. The accuracy and prediction capability of the model were verified using the validation and SEER cohorts.ResultsLASSO Cox regression analysis revealed that lymph node metastasis, molecular subtype, tumor size, chemotherapy, radiotherapy, and lung metastasis were statistically significantly correlated with BCBM. The C-indexes of the survival nomogram in the training, validation, and SEER cohorts were 0.714, 0.710, and 0.670, respectively, which showed good prediction capability. The calibration curves demonstrated that the nomogram had great forecast precision, and a dynamic diagram was drawn to increase the maneuverability of the results. The Risk Stratification System showed that the OS of low-risk patients was considerably better than that of high-risk patients ( P < 0.001).ConclusionThe nomogram prediction model constructed in this study has a good predictive value, which can effectively evaluate the survival rate of patients with postoperative BCBM.

Project description:OBJECTIVE:The lack of coordinated and appropriate healthcare across sectors has produced more patients for county hospitals in China. This study examined differences in patient choice between township and county hospitals for readmission after a first township hospitalisation, and the determinants that influenced this choice. DESIGN:A retrospective study of readmissions across hospitals after a first admission in township hospital. A township-township (TT) inpatient group and a township-county (TC) inpatient group were compared. A two-level logistic regression model was used to examine the determinants of choice for hospital readmission. SETTING:Data were drawn from a population-based health utilisation database for Qianjiang District, China, from 1 January 2008 to 31 December 2013. PARTICIPANTS:This study focused on readmitted patients whose first admission was in a township hospital. Readmission cases were identified as the same diagnosis (International Classification of Diseases, Tenth Revision) in a subsequent hospitalisation within 30 days. In total, 6764 readmissions had first admissions in township hospitals. PRIMARY OUTCOME MEASURES:Patient choice for hospital readmission after a first township hospitalisation. RESULTS:The TT group accounted for 62.5% (4225) and the TC group for 37.5% (2539) of readmissions in 6 years, and the proportion of TC readmissions in total inpatients increased from 1.66% to 1.89%. Readmission rates varied among towns (p<0.001). Differences between the TC and TT groups included: length of stay (LOS) of first admission (6.96 days vs 9.23 days), average interval between admissions (6.03 days vs 14.95 days) and disease category. Admission year, age, travel time to county hospital, interval between admissions, first admission LOS and disease category were determinants of choice for hospital readmission. CONCLUSIONS:Patients whose first admission was in a township hospital were more likely to be readmitted to a county hospital. A combination of first LOS and interval between admissions may be an effective identification index for TC readmission. TRIAL REGISTRATION NUMBER:ChiCTR-OOR-14005563.

Project description:BackgroundRheumatic diseases can seriously impact children's general health, development, and growth. However, due to a lack of resources, paediatric rheumatology is a largely underdeveloped speciality in many African nations. Children with rheumatic disorders face obstacles in accessing specialized medical care, including lack of specialists, care centres, medication access, and limited research and education to increase understanding of paediatric rheumatic disease among healthcare practitioners. This study described the disease characteristics, prevalence, and challenges faced by paediatric rheumatic disease patients receiving care at a teaching hospital in Accra, Ghana.MethodsA retrospective record-based study was conducted among all paediatric cases presenting to the rheumatology clinic of the Korle Bu Teaching Hospital (KBTH) from January 2011 to December 2021. Data collected include clinical features, laboratory findings at disease presentation, andtherapeutic regimens prescribed per standard guidelines and experiences.ResultsA total of 121 cases were identified as of 2021, indicating a point prevalence of 0.0011%. The majority (73%) were females with a mean age of 13.4 ± 3.2 years. The mean duration of symptoms in months experienced by patients before being successfully referred to a rheumatologist was 18 months. There were significant differences between referred and confirmed diagnoses, especially in cases involving mixed connective tissue diseases (MCTD), systemic lupus erythematosus (SLE), and juvenile dermatomyositis (JDM), suggesting that these conditions may be under-recognised. Arthralgia and arthritis were the most common presenting symptoms. More than three-quarters (86.8%) of the cases studied were treated with steroids (oral or intravenous). In cases requiring immunosuppressive therapy, methotrexate was the most commonly prescribed in 33.9% of instances. Mortality was recorded at 8.3%, with the majority involving SLE cases. Most (95.7%) of the primary caregivers expressed positive experiences regarding care received at the adult rheumatology clinic.ConclusionThere were significant delays in diagnosis and diagnostic accuracy for patients with paediatric rheumatic disease (PRD). This highlights the pressing need for strengthening paediatric rheumatology services in Africa, including increasing awareness about these conditions among the public and healthcare providers to improve early diagnosis and quality of life for children with these conditions.

Project description:BackgroundUnscheduled emergency department return visits (EDRVs) are key indicators for monitoring the quality of emergency medical care. A high return rate implies that the medical services provided by the emergency department (ED) failed to achieve the expected results of accurate diagnosis and effective treatment. Older adults are more susceptible to diseases and comorbidities than younger adults, and they exhibit unique and complex clinical characteristics that increase the difficulty of clinical diagnosis and treatment. Older adults also use more emergency medical resources than people in other age groups. Many studies have reviewed the causes of EDRVs among general ED patients; however, few have focused on older adults, although this is the age group with the highest rate of EDRVs.ObjectiveThis aim of this study is to establish a model for predicting unscheduled EDRVs within a 72-hour period among patients aged 65 years and older. In addition, we aim to investigate the effects of the influencing factors on their unscheduled EDRVs.MethodsWe used stratified and randomized data from Taiwan's National Health Insurance Research Database and applied data mining techniques to construct a prediction model consisting of patient, disease, hospital, and physician characteristics. Records of ED visits by patients aged 65 years and older from 1996 to 2010 in the National Health Insurance Research Database were selected, and the final sample size was 49,252 records.ResultsThe decision tree of the prediction model achieved an acceptable overall accuracy of 76.80%. Economic status, chronic illness, and length of stay in the ED were the top three variables influencing unscheduled EDRVs. Those who stayed in the ED overnight or longer on their first visit were less likely to return. This study confirms the results of prior studies, which found that economically underprivileged older adults with chronic illness and comorbidities were more likely to return to the ED.ConclusionsMedical institutions can use our prediction model as a reference to improve medical management and clinical services by understanding the reasons for 72-hour unscheduled EDRVs in older adult patients. A possible solution is to create mechanisms that incorporate our prediction model and develop a support system with customized medical education for older patients and their family members before discharge. Meanwhile, a reasonably longer length of stay in the ED may help evaluate treatments and guide prognosis for older adult patients, and it may further reduce the rate of their unscheduled EDRVs.

Dataset Information

Predicting risk of hospitalisation: a retrospective population-based analysis in a paediatric population in Emilia-Romagna, Italy.

Publications

Predicting risk of hospitalisation: a retrospective population-based analysis in a paediatric population in Emilia-Romagna, Italy.

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