Dataset Information

Repeated Critical Illness and Unplanned Readmissions Within 1 Year to PICUs.

ABSTRACT: OBJECTIVES:To determine the occurrence rate of unplanned readmissions to PICUs within 1 year and examine risk factors associated with repeated readmission. DESIGN:Retrospective cohort analysis. SETTING:Seventy-six North American PICUs that participated in the Virtual Pediatric Systems, LLC (VPS, LLC, Los Angeles, CA). PATIENTS:Ninety-three thousand three hundred seventy-nine PICU patients discharged between 2009 and 2010. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:Index admissions and unplanned readmissions were characterized and their outcomes compared. Time-to-event analyses were performed to examine factors associated with readmission within 1 year. Eleven percent (10,233) of patients had 15,625 unplanned readmissions within 1 year to the same PICU; 3.4% had two or more readmissions. Readmissions had significantly higher PICU mortality and longer PICU length of stay, compared with index admissions (4.0% vs 2.5% and 2.5 vs 1.6 d; all p < 0.001). Median time to readmission was 30 days for all readmissions, 3.5 days for readmissions during the same hospitalization, and 66 days for different hospitalizations. Having more complex chronic conditions was associated with earlier readmission (adjusted hazard ratio, 2.9 for one complex chronic condition; hazard ratio, 4.8 for two complex chronic conditions; hazard ratio, 9.6 for three or more complex chronic conditions; all p < 0.001 compared no complex chronic condition). Most specific complex chronic condition conferred a greater risk of readmission, and some had considerably higher risk than others. CONCLUSIONS:Unplanned readmissions occurred in a sizable minority of PICU patients. Patients with complex chronic conditions and particular conditions were at much higher risk for readmission.

SUBMITTER: Edwards JD

PROVIDER: S-EPMC5541898 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Repeated Critical Illness and Unplanned Readmissions Within 1 Year to PICUs.

Edwards Jeffrey D JD Lucas Adam R AR Boscardin W John WJ Dudley R Adams RA

Critical care medicine 20170801 8

<h4>Objectives</h4>To determine the occurrence rate of unplanned readmissions to PICUs within 1 year and examine risk factors associated with repeated readmission.<h4>Design</h4>Retrospective cohort analysis.<h4>Setting</h4>Seventy-six North American PICUs that participated in the Virtual Pediatric Systems, LLC (VPS, LLC, Los Angeles, CA).<h4>Patients</h4>Ninety-three thousand three hundred seventy-nine PICU patients discharged between 2009 and 2010.<h4>Interventions</h4>None.<h4>Measurements an ...[more]

PMID: 28708677

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Project description:Preventing rehospitalizations for patients with serious chronic illnesses is a focus of national quality initiatives. Although 8 million people are admitted yearly to an ICU, the frequency of rehospitalizations (readmissions to the hospital after discharge) is unknown. We sought to determine the frequency of rehospitalization after an ICU stay, outcomes for rehospitalized patients, and factors associated with rehospitalization.Retrospective cohort study using the New York Statewide Planning and Research Cooperative System, an administrative database of all hospital discharges in New York State.ICUs in New York State.ICU patients who survived to hospital discharge in 2008-2010.None.Primary outcome was the cumulative incidence of first early rehospitalization (within 30 days of discharge), and secondary outcome was the cumulative incidence of late rehospitalization (between 31 and 180 d). Factors associated with rehospitalization within both time periods were identified using competing risk regression models. Of 492,653 ICU patients, 79,960 had a first early rehospitalization (cumulative incidence, 16.2%) and an additional 73,250 late rehospitalizations (cumulative incidence, 18.9%). Over one quarter of all rehospitalizations (28.6% for early; 26.7% for late) involved ICU admission. Overall hospital mortality for rehospitalized patients was 7.6% for early and 4.6% for late rehospitalizations. Longer index hospitalization (adjusted hazard ratio, 1.61; 95% CI, 1.57-1.66 for 7-13 d vs < 3 d), discharge to a skilled nursing facility versus home (adjusted hazard ratio, 1.54; 95% CI, 1.51-1.58), and having metastatic cancer (adjusted hazard ratio, 1.46; 95% CI, 1.41-1.51) were associated with the greatest hazard of early rehospitalization.Approximately 16% of ICU survivors were rehospitalized within 30 days of hospital discharge; rehospitalized patients had high rates of ICU admission and hospital mortality. Few characteristics were strongly associated with rehospitalization, suggesting that identifying high-risk individuals for intervention may require additional predictors beyond what is available in administrative databases.

Project description:BackgroundCare transitions are high-risk processes, especially for people with complex or chronic illness. Discharge letters are an opportunity to provide written information to improve patients' self-management after discharge. The aim of this study is to determine the impact of discharge letter content on unplanned hospital readmissions and self-rated quality of care transitions among patients 60 years of age or older with chronic illness.MethodsThe study had a convergent mixed methods design. Patients with chronic obstructive pulmonary disease or congestive heart failure were recruited from two hospitals in Region Stockholm if they were living at home and Swedish-speaking. Patients with dementia or cognitive impairment, or a "do not resuscitate" statement in their medical record were excluded. Discharge letters from 136 patients recruited to a randomised controlled trial were coded using an assessment matrix and deductive content analysis. The assessment matrix was based on a literature review performed to identify key elements in discharge letters that facilitate a safe care transition to home. The coded key elements were transformed into a quantitative variable of "SAFE-D score". Bivariate correlations between SAFE-D score and quality of care transition as well as unplanned readmissions within 30 and 90 days were calculated. Lastly, a multivariable Cox proportional hazards model was used to investigate associations between SAFE-D score and time to readmission.ResultsAll discharge letters contained at least five of eleven key elements. In less than two per cent of the discharge letters, all eleven key elements were present. Neither SAFE-D score, nor single key elements correlated with 30-day or 90-day readmission rate. SAFE-D score was not associated with time to readmission when adjusted for a range of patient characteristics and self-rated quality of care transitions.ConclusionsWhile written summaries play a role, they may not be sufficient on their own to ensure safe care transitions and effective self-care management post-discharge.Trial registrationClinical Trials. giv, NCT02823795, 01/09/2016.

Project description:ObjectivesTo identify the preventability, determinants and causes of unplanned hospital readmissions within 30 days of discharge using a multidisciplinary approach and including patients' perspectives.DesignA prospective cross-sectional single-center study.SettingUrban teaching hospital in Amsterdam, the Netherlands.Participants430 patients were included. Inclusion criteria were: age ≥ 18 years, discharged from one of seven participating clinical departments and an unplanned readmission within 30 days.MethodsResidents from the participating departments individually assessed whether the readmission was caused by healthcare, the preventability and possible causes of readmissions using a tool. Thereafter, the preventability of the cases was discussed in a multidisciplinary meeting with residents of all participating departments and clinical pharmacists. The primary outcome was the proportion of readmissions that were potentially preventable. Secondary outcomes were the determinants for a readmission, causes for preventable readmissions, the change in the final decision on preventability after the multidisciplinary meeting and the value of patient interviews in assessing preventability. Differences in characteristics of potentially preventable readmissions (PPRs) and non-PPRs were analyzed using multivariable logistic regression.ResultsOf 430 readmissions, 56 (13%) were assessed as PPRs. Age was significantly associated with a PPR (adjusted OR: 2.42; 95%, CI 1.23-4.74; p = 0.01). The main causes for PPRs were diagnostic (30%), medication (27%) and management problems (27%). During the multidisciplinary meeting, the final decision on preventability changed in 11% of the cases. When a patient interview was available, it was used as a source of information to assess preventability in 26% of readmissions. In 7% of cases, the patient interview was mentioned as the most important source.Conclusion and implications13% of readmissions were potentially preventable with diagnostic, medication or management problems being main causes. A multidisciplinary review approach and including the patient's perspective could contribute to a better understanding of the complexity of readmissions and possible improvements.

Project description:ObjectiveTo determine the costs and hospital resource use from all PICU patients readmitted with a PICU stay within 12 months of hospital index discharge.DesignCross-sectional, retrospective cohort study using Pediatric Health Information System.SettingFifty-two tertiary children's hospitals.SubjectsPediatric patients under 18 years old admitted to the PICU from January 1, 2016, to December 31, 2017.InterventionsNone.Measurements and main resultsPatient characteristics and costs of care were compared between those with readmission requiring PICU care and those with only a single PICU admission per annum. In this 2-year cohort, there were 239,157 index PICU patients of which 36,970 (15.5%) were readmitted and required PICU care during the 12 months following index admission. The total hospital cost for all index admissions and readmissions was $17.3 billion, of which 21.5% ($3.71 billion) were incurred during a readmission stay involving care in the PICU; of the 3,459,079 hospital days, 20.3% (702,200) were readmission days including those where PICU care was required. Of the readmitted patients, 11,703 (30.0%) received only PICU care, accounting for $662 million in costs and 110,215 PICU days. Although 43.6% of all costs were associated with patients who required readmission, these patients only accounted for 15.5% of the index patients and 28% of index hospitalization expenditures. More patients in the readmitted group had chronic complex conditions at index discharge compared with those not readmitted (83.9% vs 54.9%; p < 0.001). Compared with those discharged directly to home without home healthcare, patients discharged to a skilled nursing facility had 18% lower odds of readmission (odds ratio 0.82 [95% CI, 0.75-0.89]; p < 0.001) and those discharged home with home healthcare had 43% higher odds of readmission (odds ratio, 1.43 [95% CI, 1.36-1.51]; p < 0.001).ConclusionsRepeated admissions with PICU care resulted in significant direct medical costs and resource use for U.S. children's hospitals.

Project description:OBJECTIVE:To synthesise evidence on risk factors associated with paediatric unplanned hospital readmissions (UHRs). DESIGN:Systematic review. DATA SOURCE:CINAHL, EMBASE (Ovid) and MEDLINE from 2000 to 2017. ELIGIBILITY CRITERIA:Studies published in English with full-text access and focused on paediatric All-cause, Surgical procedure and General medical condition related UHRs were included. DATA EXTRACTION AND SYNTHESIS:Characteristics of the included studies, examined variables and the statistically significant risk factors were extracted. Two reviewers independently assessed study quality based on six domains of potential bias. Pooling of extracted risk factors was not permitted due to heterogeneity of the included studies. Data were synthesised using content analysis and presented in narrative form. RESULTS:Thirty-six significant risk factors were extracted from the 44 included studies and presented under three health condition groupings. For All-cause UHRs, ethnicity, comorbidity and type of health insurance were the most frequently cited factors. For Surgical procedure related UHRs, specific surgical procedures, comorbidity, length of stay (LOS), age, the American Society of Anaesthesiologists class, postoperative complications, duration of procedure, type of health insurance and illness severity were cited more frequently. The four most cited risk factors associated with General medical condition related UHRs were comorbidity, age, health service usage prior to the index admission and LOS. CONCLUSIONS:This systematic review acknowledges the complexity of readmission risk prediction in paediatric populations. This review identified four risk factors across all three health condition groupings, namely comorbidity; public health insurance; longer LOS and patients<12 months or between 13-18 years. The identification of risk factors, however, depended on the variables examined by each of the included studies. Consideration should be taken into account when generalising reported risk factors to other institutions. This review highlights the need to develop a standardised set of measures to capture key hospital discharge variables that predict unplanned readmission among paediatric patients.

Project description:Background and objectivesPatients on hemodialysis have high 30-day unplanned readmission rates. Using a national all-payer administrative database, we describe the epidemiology of 30-day unplanned readmissions in patients on hemodialysis, determine concordance of reasons for initial admission and readmission, and identify predictors for readmission.Design, setting, participants, & measurementsThis is a retrospective cohort study using the Nationwide Readmission Database from the year 2013 to identify index admissions and readmission in patients with ESRD on hemodialysis. The Clinical Classification Software was used to categorize admission diagnosis into mutually exclusive clinically meaningful categories and determine concordance of reasons for admission on index hospitalizations and readmissions. Survey logistic regression was used to identify predictors of at least one readmission.ResultsDuring 2013, there were 87,302 (22%) index admissions with at least one 30-day unplanned readmission. Although patient and hospital characteristics were statistically different between those with and without readmissions, there were small absolute differences. The highest readmission rate was for acute myocardial infarction (25%), whereas the lowest readmission rate was for hypertension (20%). The primary reasons for initial hospitalization and subsequent 30-day readmission were discordant in 80% of admissions. Comorbidities that were associated with readmissions included depression (odds ratio, 1.10; 95% confidence interval [95% CI], 1.05 to 1.15; P<0.001), drug abuse (odds ratio, 1.41; 95% CI, 1.31 to 1.51; P<0.001), and discharge against medical advice (odds ratio, 1.57; 95% CI, 1.45 to 1.70; P<0.001). A group of high utilizers, which constituted 2% of the population, was responsible for 20% of all readmissions.ConclusionsIn patients with ESRD on hemodialysis, nearly one quarter of admissions were followed by a 30-day unplanned readmission. Most readmissions were for primary diagnoses that were different from initial hospitalization. A small proportion of patients accounted for a disproportionate number of readmissions.

Project description:BackgroundTotal joint replacements are high-volume and high-cost procedures that should be monitored for cost and quality control. Models that can identify patients at high risk of readmission might help reduce costs by suggesting who should be enrolled in preventive care programs. Previous models for risk prediction have relied on structured data of patients rather than clinical notes in electronic health records (EHRs). The former approach requires manual feature extraction by domain experts, which may limit the applicability of these models.ObjectiveThis study aims to develop and evaluate a machine learning model for predicting the risk of 30-day readmission following knee and hip arthroplasty procedures. The input data for these models come from raw EHRs. We empirically demonstrate that unstructured free-text notes contain a reasonably predictive signal for this task.MethodsWe performed a retrospective analysis of data from 7174 patients at Partners Healthcare collected between 2006 and 2016. These data were split into train, validation, and test sets. These data sets were used to build, validate, and test models to predict unplanned readmission within 30 days of hospital discharge. The proposed models made predictions on the basis of clinical notes, obviating the need for performing manual feature extraction by domain and machine learning experts. The notes that served as model inputs were written by physicians, nurses, pathologists, and others who diagnose and treat patients and may have their own predictions, even if these are not recorded.ResultsThe proposed models output readmission risk scores (propensities) for each patient. The best models (as selected on a development set) yielded an area under the receiver operating characteristic curve of 0.846 (95% CI 82.75-87.11) for hip and 0.822 (95% CI 80.94-86.22) for knee surgery, indicating reasonable discriminative ability.ConclusionsMachine learning models can predict which patients are at a high risk of readmission within 30 days following hip and knee arthroplasty procedures on the basis of notes in EHRs with reasonable discriminative power. Following further validation and empirical demonstration that the models realize predictive performance above that which clinical judgment may provide, such models may be used to build an automated decision support tool to help caretakers identify at-risk patients.

Dataset Information

Repeated Critical Illness and Unplanned Readmissions Within 1 Year to PICUs.

Publications

Repeated Critical Illness and Unplanned Readmissions Within 1 Year to PICUs.

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