A Predictive Risk Model for Infection-Related Hospitalization Among Home Healthcare Patients.
Ontology highlight
ABSTRACT:
SUBMITTER: Shang J
PROVIDER: S-EPMC7477895 | biostudies-literature | 2020 May/Jun
REPOSITORIES: biostudies-literature
Publications
A Predictive Risk Model for Infection-Related Hospitalization Among Home Healthcare Patients.
Shang Jingjing J Russell David D Dowding Dawn D McDonald Margaret V MV Murtaugh Christopher C Liu Jianfang J Larson Elaine L EL Sridharan Sridevi S Brickner Carlin C
Journal for healthcare quality : official publication of the National Association for Healthcare Quality 20200501 3
Infection prevention is a high priority for home healthcare (HHC), but tools are lacking to identify patients at highest risk of developing infections. The purpose of this study was to develop and test a predictive risk model to identify HHC patients at risk of an infection-related hospitalization or emergency department visit. A nonexperimental study using secondary data was conducted. The Outcome and Assessment Information Set linked with relevant clinical data from 112,788 HHC admissions in 2 ...[more]
Similar Datasets
Project description:BackgroundHome hospitalization is widely accepted as a cost-effective alternative to conventional hospitalization for selected patients. A recent analysis of the home hospitalization and early discharge (HH/ED) program at Hospital Clínic de Barcelona over a 10-year period demonstrated high levels of acceptance by patients and professionals, as well as health value-based generation at the provider and health-system levels. However, health risk assessment was identified as an unmet need with the potential to enhance clinical decision making.ObjectiveThe objective of this study is to generate and assess predictive models of mortality and in-hospital admission at entry and at HH/ED discharge.MethodsPredictive modeling of mortality and in-hospital admission was done in 2 different scenarios: at entry into the HH/ED program and at discharge, from January 2009 to December 2015. Multisource predictive variables, including standard clinical data, patients' functional features, and population health risk assessment, were considered.ResultsWe studied 1925 HH/ED patients by applying a random forest classifier, as it showed the best performance. Average results of the area under the receiver operating characteristic curve (AUROC; sensitivity/specificity) for the prediction of mortality were 0.88 (0.81/0.76) and 0.89 (0.81/0.81) at entry and at home hospitalization discharge, respectively; the AUROC (sensitivity/specificity) values for in-hospital admission were 0.71 (0.67/0.64) and 0.70 (0.71/0.61) at entry and at home hospitalization discharge, respectively.ConclusionsThe results showed potential for feeding clinical decision support systems aimed at supporting health professionals for inclusion of candidates into the HH/ED program, and have the capacity to guide transitions toward community-based care at HH discharge.
| S-EPMC7578817 | biostudies-literature
Project description:BackgroundAbout one in five patients receiving home healthcare (HHC) services are hospitalized or visit an emergency department (ED) during a home care episode. Early identification of at-risk patients can prevent these negative outcomes. However, risk indicators, including language in clinical notes that indicate a concern about a patient, are often hidden in narrative documentation throughout their HHC episode.ObjectiveThe aim of the study was to develop an automated natural language processing (NLP) algorithm to identify concerning language indicative of HHC patients' risk of hospitalizations or ED visits.MethodsThis study used the Omaha System-a standardized nursing terminology that describes problems/signs/symptoms that can occur in the community setting. First, five HHC experts iteratively reviewed the Omaha System and identified concerning concepts indicative of HHC patients' risk of hospitalizations or ED visits. Next, we developed and tested an NLP algorithm to identify these concerning concepts in HHC clinical notes automatically. The resulting NLP algorithm was applied on a large subset of narrative notes (2.3 million notes) documented for 66,317 unique patients ( n = 87,966 HHC episodes) admitted to one large HHC agency in the Northeast United States between 2015 and 2017.ResultsA total of 160 Omaha System signs/symptoms were identified as concerning concepts for hospitalizations or ED visits in HHC. These signs/symptoms belong to 31 of the 42 available Omaha System problems. Overall, the NLP algorithm showed good performance in identifying concerning concepts in clinical notes. More than 18% of clinical notes were detected as having at least one concerning concept, and more than 90% of HHC episodes included at least one Omaha System problem. The most frequently documented concerning concepts were pain, followed by issues related to neuromusculoskeletal function, circulation, mental health, and communicable/infectious conditions.ConclusionOur findings suggest that concerning problems or symptoms that could increase the risk of hospitalization or ED visit were frequently documented in narrative clinical notes. NLP can automatically extract information from narrative clinical notes to improve our understanding of care needs in HHC. Next steps are to evaluate which concerning concepts identified in clinical notes predict hospitalization or ED visit.
| S-EPMC9246992 | biostudies-literature
Project description:ImportanceHospitalizations for infections among nursing home (NH) residents remain common despite national initiatives to reduce them. Cognitive impairment, which markedly affects quality of life and caregiving needs, has been associated with hospitalizations, but the association between infection-related hospitalizations and long-term cognitive function among NH residents is unknown.ObjectiveTo examine whether there are changes in cognitive function before vs after infection-related hospitalizations among NH residents.Design, setting, and participantsThis cohort study used data from the Minimum Data Set 3.0 linked to Medicare hospitalization data from 2011 to 2017 for US nursing home residents aged 65 years or older who had experienced an infection-related hospitalization and had at least 2 quarterly Minimum Data Set assessments before and 4 or more after the infection-related hospitalization. Analyses were performed from September 1, 2019, to December 21, 2020.ExposureInfection-related hospitalization lasting 1 to 14 days.Main outcomes and measuresUsing an event study approach, associations between infection-related hospitalizations and quarterly changes in cognitive function among NH residents were examined overall and by sex, age, Alzheimer disease and related dementias (ADRD) diagnosis, and sepsis vs other infection-related diagnoses. Resident-level cognitive function was measured using the Cognitive Function Scale (CFS), with scores ranging from 1 (intact) to 4 (severe cognitive impairment).ResultsOf the sample of 20 698 NH residents, 71.0% were women and 82.6% were non-Hispanic White individuals; the mean (SD) age at the time of transfer to the hospital was 82 (8.5) years. The mean CFS score was 2.17, and the prevalence of severe cognitive impairment (CFS score, 4) was 9.0%. During the first quarter after an infection-related hospitalization, residents experienced a mean increase of 0.06 points in CFS score (95% CI, 0.05-0.07 points; P < .001), or 3%. The increase in scores was greatest among residents aged 85 years or older vs younger residents by approximately 0.022 CFS points (95% CI, 0.004-0.040 points; P < .05). The prevalence of severe cognitive impairment increased by 1.6 percentage points (95% CI, 1.2-2.0 percentage points; P < .001), or 18%; the increases were observed among individuals with ADRD but not among those without it. After an infection-related hospitalization, cognition among residents who had experienced sepsis declined more than for residents who had not by about 0.02 CFS points (95% CI, 0.00-0.04 points; P < .05). All observed differences persisted without an accelerated rate of decline for at least 6 quarters after infection-related hospitalization. No differences were observed by sex.Conclusions and relevanceIn this cohort study, infection-related hospitalization was associated with immediate and persistent cognitive decline among nursing home residents, with the largest increase in CFS scores among older residents, those with ADRD, and those who had experienced sepsis. Identification of NH residents at risk of worsened cognition after an infection-related hospitalization may help to ensure that their care needs are addressed to prevent further cognitive decline.
| S-EPMC8065379 | biostudies-literature
Project description:Hospital workers are at high risk of contact with COVID-19 patients. Currently, there is no evidence-based, comprehensive risk assessment tool for healthcare-related exposure; so, we aimed to identify independent factors related to COVID-19 infection in hospital workers following workplace exposure(s) and construct a risk prediction model. We analyzed the COVID-19 contact tracing dataset from 15 July to 31 December 2021 using multiple logistic regression analysis, considering exposure details, demographics, and vaccination history. Of 7146 included exposures to confirmed COVID-19 patients, 229 (4.2%) had subsequently tested positive via RT-PCR. Independent risk factors for a positive test were having symptoms (adjusted odds ratio 4.94, 95%CI 3.83-6.39), participating in an unprotected aerosol-generating procedure (aOR 2.87, 1.66-4.96), duration of exposure >15 min (aOR 2.52, 1.82-3.49), personnel who did not wear a mask (aOR 2.49, 1.75-3.54), exposure to aerodigestive secretion (aOR 1.5, 1.03-2.17), index patient not wearing a mask (aOR 1.44, 1.01-2.07), and exposure distance <1 m without eye protection (aOR 1.39, 1.02-1.89). High-potency vaccines and high levels of education protected against infection. A risk model and scoring system with good discrimination power were built. Having symptoms, unprotected exposure, lower education level, and receiving low potency vaccines increased the risk of laboratory-confirmed COVID-19 following healthcare-related exposure events.
| S-EPMC9505412 | biostudies-literature
Project description:BACKGROUND:Moderate kidney disease may predispose to infection. We sought to determine whether decreased kidney function, estimated by serum cystatin C level, was associated with the risk of infection-related hospitalization in older individuals. STUDY DESIGN:Cohort study. SETTING & PARTICIPANTS:5,142 Cardiovascular Health Study (CHS) participants with measured serum creatinine and cystatin C and without estimated glomerular filtration rate (eGFR) <15 mL/min/1.73 m(2) at enrollment. PREDICTOR:The primary exposure of interest was eGFR using serum cystatin C level (eGFR(SCysC)). OUTCOME:Infection-related hospitalizations during a median follow-up of 11.5 years. RESULTS:In adjusted analyses, eGFR(SCysC) categories of 60-89, 45-59, and 15-44 mL/min/1.73 m(2) were associated with 16%, 37%, and 64% greater risk of all-cause infection-related hospitalization, respectively, compared with eGFR(SCysC) ?90 mL/min/1.73 m(2). When cause-specific infection was examined, eGFR(SCysC) of 15-44 mL/min/1.73 m(2) was associated with an 80% greater risk of pulmonary and 160% greater risk of genitourinary infection compared with eGFR(SCysC) ?90 mL/min/1.73 m(2). LIMITATIONS:No measures of urinary protein, study limited to principal discharge diagnosis. CONCLUSIONS:Lower kidney function, estimated using cystatin C level, was associated with a linear and graded risk of infection-related hospitalization. These findings highlight that even moderate degrees of decreased kidney function are associated with clinically significant higher risks of serious infection in older individuals.
| S-EPMC3288732 | biostudies-literature
Project description:Background and objectivesInfection-related hospitalizations have increased dramatically over the last 10 years in patients receiving in-center hemodialysis. Patient and dialysis facility characteristics associated with the rate of infection-related hospitalization were examined, with consideration of the region of care, rural-urban residence, and socioeconomic status.Design, setting, participants, & measurementsThe US Renal Data System linked to the American Community Survey and Rural-Urban Commuting Area codes was used to examine factors associated with hospitalization for infection among Medicare beneficiaries starting in-center hemodialysis between 2005 and 2008. A Poisson mixed effects model was used to examine the associations among patient and dialysis facility characteristics and the rate of infection-related hospitalization.ResultsAmong 135,545 Medicare beneficiaries, 38,475 (28%) had at least one infection-related hospitalization. The overall rate of infection-related hospitalization was 40.2 per 100 person-years. Age ≥ 85 years old, cancer, chronic obstructive pulmonary disease, inability to ambulate or transfer, drug dependence, residence in a care facility, serum albumin <3.5 g/dl at dialysis initiation, and dialysis initiation with an access other than a fistula were associated with a ≥ 20% increase in the rate of infection-related hospitalization. Patients residing in isolated small rural compared with urban areas had lower rates of hospitalization for infection (rate ratio, 0.91; 95% confidence interval, 0.86 to 0.97), and rates of hospitalization for infection varied across the ESRD networks. Measures of socioeconomic status (at the zip code level), total facility staffing, and the composition of staff (percentage of nurses) were not associated with the rate of hospitalization for infection.ConclusionsPatient and facility factors associated with higher rates of infection-related hospitalization were identified. The findings from this study can be used to identify patients at higher risk for infection and inform the design of infection prevention strategies.
| S-EPMC4670763 | biostudies-literature
Project description:BackgroundAbout 30% of home healthcare patients are hospitalized or visit an emergency department (ED) during a home healthcare (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes.ObjectivesThe aim of the study was to identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes).MethodsThis study used a large database of HHC visit notes (n = 727,676) documented for 112,237 HHC episodes (89,459 unique patients) by clinicians of the largest nonprofit HHC agency in the United States. Text mining and machine learning algorithms (Naïve Bayes, decision tree, random forest) were implemented to predict patient hospitalization or ED visits using the content of clinical notes. Risk factors associated with hospitalization or ED visits were identified using a feature selection technique (gain ratio attribute evaluation).ResultsBest performing text mining method (random forest) achieved good predictive performance. Seven risk factors categories were identified, with clinical factors, coordination/communication, and service use being the most frequent categories.DiscussionThis study was the first to explore the potential contribution of HHC clinical notes to identifying patients at risk for hospitalization or an ED visit. Our results suggest that HHC visit notes are highly informative and can contribute significantly to identification of patients at risk. Further studies are needed to explore ways to improve risk prediction by adding more data elements from additional data sources.
| S-EPMC7606545 | biostudies-literature
Project description:Background:Preventing hospitalizations and re-hospitalizations of older adults receiving Medicare home health (HH) services is a key goal for patients and care providers. This study aimed to identify factors related to greater risk of and earlier hospitalizations from HH, a key step in targeting preventive efforts. Methods:Data come from Medicare mandated start-of-care assessments from 87,780 HH patients served by 132 agencies in 32 states, collected from January 2013 to March 2015. Using parametric accelerated failure time (AFT) survival models, we evaluated the association between key patient and environmental characteristics and the hazard of and time until hospitalization and re-hospitalization. Results:In total, 15,030 hospitalizations, including 6,539 re-hospitalizations, occurred in the sample within 60 days of start of HH. Factors most strongly associated with substantially greater risk of and earlier hospitalization included male gender, history of hospitalization, polypharmacy, elevated depressive symptoms, greater functional disability, primary diagnoses of heart disease, chronic obstructive pulmonary disease, and urinary tract infection, and government-controlled agency care. In addition to these factors, black race and primary diagnosis of skin wounds were uniquely related to risk of earlier re-hospitalization. Conclusions:Results suggest that factors collected during routine HH patient assessments can provide important information to predict risk of earlier hospitalization and re-hospitalization among Medicare HH patients. Identified factors can help identify patients at greatest risk of early hospitalization and may be important targets for agencies and care providers to prevent avoidable hospitalizations.
| S-EPMC6093315 | biostudies-other
Project description:While home dialysis is being promoted, there are few comparative effectiveness studies of home-based modalities to guide patient decisions. To address this, we matched 1116 daily home hemodialysis (DHD) patients by propensity scores to 2784 contemporaneous USRDS patients receiving home peritoneal dialysis (PD), and compared hospitalization rates from cardiovascular, infectious, access-related or bleeding causes (prespecified composite), and modality failure risk. We performed similar analyses for 1187 DHD patients matched to 3173 USRDS patients receiving in-center conventional hemodialysis (CHD). The composite hospitalization rate was significantly lower with DHD than with PD (0.93 vs. 1.35/patient-year, hazard ratio=0.73 (95% CI=0.67-0.79)). DHD patients spent significantly fewer days in hospital than PD patients (5.2 vs. 9.2 days/patient-year), and significantly more DHD patients remained admission-free (52% DHD vs. 32% PD). In contrast, there was no significant difference in hospitalizations between DHD and CHD (DHD vs. CHD: 0.93 vs. 1.10/patient-year, hazard ratio 0.92 (0.85-1.00)). Cardiovascular hospitalizations were lower with DHD than with CHD (0.68 (0.61-0.77)), while infectious and access hospitalizations were higher (1.15 (1.04-1.29) and 1.25 (1.08-1.43), respectively). Significantly more PD than DHD patients switched back to in-center HD (44% vs. 15%; 3.4 (2.9-4.0)). In this prevalent cohort, home DHD was associated with fewer admissions and hospital days than PD, and a substantially lower risk of modality failure.
| S-EPMC4526768 | biostudies-other
Project description:Chemotherapy-related hospitalizations in patients with advanced cancer are common, distressing, and costly. Methods to identify patients at high risk of chemotherapy toxic effects will permit development of targeted strategies to prevent chemotherapy-related hospitalizations.To demonstrate the feasibility of using readily available clinical data to assess patient-specific risk of chemotherapy-related hospitalization.Nested case-control study conducted from January 2003 through December 2011 at the Mass General/North Shore Cancer Center, a community-based cancer center in northeastern Massachusetts. The parent cohort included 1579 consecutive patients with advanced solid-tumor cancer receiving palliative-intent chemotherapy. Case patients (n?=?146) included all patients from the parent cohort who experienced a chemotherapy-related hospitalization. Controls (n?=?292) were randomly selected from 1433 patients who did not experience a chemotherapy-related hospitalization.Putative risk factors for chemotherapy-related hospitalization-including patient characteristics, treatment characteristics, and pretreatment laboratory values-were abstracted from medical records. Multivariable logistic regression was used to model the patient-specific risk of chemotherapy-related hospitalization.Chemotherapy-related hospitalization, as adjudicated by the oncology clinical care team within a systematic quality-assessment program.A total of 146 (9.2%) of 1579 patients from the parent cohort experienced a chemotherapy-related hospitalization. In multivariate regression, 7 variables were significantly associated with chemotherapy-related hospitalization: age, Charlson comorbidity score, creatinine clearance, calcium level, below-normal white blood cell and/or platelet count, polychemotherapy (vs monotherapy), and receipt of camptothecin chemotherapy. The median predicted risk of chemotherapy-related hospitalization was 6.0% (interquartile range [IQR], 3.6%-11.4%) in control patients and 14.7% (IQR, 6.8%-22.5%) in case patients. The bootstrap-adjusted C statistic was 0.71 (95% CI, 0.66-0.75). At a risk threshold of 15%, the model exhibited a sensitivity of 49% (95% CI, 41%-57%) and a specificity of 85% (95% CI, 81%-89%) for predicting chemotherapy-related hospitalization.In patients initiating palliative chemotherapy for cancer, readily available clinical data were associated with the patient-specific risk of chemotherapy-related hospitalization. External validation and evaluation in the context of a clinical decision support tool are warranted.
| S-EPMC4576342 | biostudies-literature