Healthcare-associated infections in Dutch hospitals during the COVID-19 pandemic.

BACKGROUND: During the COVID-19 pandemic hospitals reorganized their resources and delivery of care, which may have affected the number of healthcare-associated infections (HAIs). We aimed to quantify changes in trends in the number of HAIs in Dutch hospitals during the COVID-19 pandemic. METHODS: National surveillance data from 2016 to 2020 on the prevalence of HAIs measured by point prevalence surveys, and the incidence of surgical site infections (SSIs) and catheter-related bloodstream infections (CRBSIs) were used to compare rates between the pre-pandemic (2016-February 2020) and pandemic (March 2020-December 2020) period. RESULTS: The total HAI prevalence among hospitalised patients was higher during the pandemic period (7.4%) compared to pre-pandemic period (6.4%), mainly because of an increase in ventilator-associated pneumonia (VAP), gastro-intestinal infections (GIs) and central nervous system (CNS) infections. No differences in SSI rates were observed during the pandemic, except for a decrease after colorectal surgeries (6.3% (95%-CI 6.0-6.6%) pre-pandemic versus 4.4% (95%-CI 3.9-5.0%) pandemic). The observed CRBSI incidence in the pandemic period (4.0/1,000 CVC days (95%-CI 3.2-4.9)) was significantly higher than predicted based on pre-pandemic trends (1.4/1000 (95%-CI 1.0-2.1)), and was increased in both COVID-19 patients and non-COVID-19 patients at the intensive care unit (ICU). CONCLUSIONS: Rates of CRBSIs, VAPs, GIs and CNS infections among hospitalised patients increased during the first year of the pandemic. Higher CRBSI rates were observed in both COVID-19 and non-COVID-19 ICU population. The full scope and influencing factors of the pandemic on HAIs needs to be studied in further detail.

Reliability and validity of multicentre surveillance of surgical site infections after colorectal surgery.

BACKGROUND: Surveillance is the cornerstone of surgical site infection prevention programs. The validity of the data collection and awareness of vulnerability to inter-rater variation is crucial for correct interpretation and use of surveillance data. The aim of this study was to investigate the reliability and validity of surgical site infection (SSI) surveillance after colorectal surgery in the Netherlands. METHODS: In this multicentre prospective observational study, seven Dutch hospitals performed SSI surveillance after colorectal surgeries performed in 2018 and/or 2019. When executing the surveillance, a local case assessment was performed to calculate the overall percentage agreement between raters within hospitals. Additionally, two case-vignette assessments were performed to estimate intra-rater and inter-rater reliability by calculating a weighted Cohen's Kappa and Fleiss' Kappa coefficient. To estimate the validity, answers of the two case-vignettes questionnaires were compared with the answers of an external medical panel. RESULTS: 1111 colorectal surgeries were included in this study with an overall SSI incidence of 8.8% (n = 98). From the local case assessment it was estimated that the overall percent agreement between raters within a hospital was good (mean 95%, range 90-100%). The Cohen's Kappa estimated for the intra-rater reliability of case-vignette review varied from 0.73 to 1.00, indicating substantial to perfect agreement. The inter-rater reliability within hospitals showed more variation, with Kappa estimates ranging between 0.61 and 0.94. In total, 87.9% of the answers given by the raters were in accordance with the medical panel. CONCLUSIONS: This study showed that raters were consistent in their SSI-ascertainment (good reliability), but improvements can be made regarding the accuracy (moderate validity). Accuracy of surveillance may be improved by providing regular training, adapting definitions to reduce subjectivity, and by supporting surveillance through automation.

Information technology aspects of large-scale implementation of automated surveillance of healthcare-associated infections.

INTRODUCTION: Healthcare-associated infections (HAI) are a major public health concern. Monitoring of HAI rates, with feedback, is a core component of infection prevention and control programmes. Digitalization of healthcare data has created novel opportunities for automating the HAI surveillance process to varying degrees. However, methods are not standardized and vary widely between different healthcare facilities. Most current automated surveillance (AS) systems have been confined to local settings, and practical guidance on how to implement large-scale AS is needed. METHODS: This document was written by a task force formed in March 2019 within the PRAISE network (Providing a Roadmap for Automated Infection Surveillance in Europe), gathering experts in HAI surveillance from ten European countries. RESULTS: The document provides an overview of the key e-health aspects of implementing an AS system of HAI in a clinical environment to support both the infection prevention and control team and information technology (IT) departments. The focus is on understanding the basic principles of storage and structure of healthcare data, as well as the general organization of IT infrastructure in surveillance networks and participating healthcare facilities. The fundamentals of data standardization, interoperability and algorithms in relation to HAI surveillance are covered. Finally, technical aspects and practical examples of accessing, storing and sharing healthcare data within a HAI surveillance network, as well as maintenance and quality control of such a system, are discussed. CONCLUSIONS: With the guidance given in this document, along with the PRAISE roadmap and governance documents, readers will find comprehensive support to implement large-scale AS in a surveillance network.

Governance aspects of large-scale implementation of automated surveillance of healthcare-associated infections.

OBJECTIVES: Surveillance of healthcare-associated infections (HAI) is increasingly automated by applying algorithms to routine-care data stored in electronic health records. Hitherto, initiatives have mainly been confined to single healthcare facilities and research settings, leading to heterogeneity in design. The PRAISE network - Providing a Roadmap for Automated Infection Surveillance in Europe - designed a roadmap to provide guidance on how to move automated surveillance (AS) from the research setting to large-scale implementation. Supplementary to this roadmap, we here discuss the governance aspects of automated HAI surveillance within networks, aiming to support both the coordinating centres and participating healthcare facilities as they set up governance structures and to enhance involvement of legal specialists. METHODS: This article is based on PRAISE network discussions during two workshops. A taskforce was installed that further elaborated governance aspects for AS networks by reviewing documents and websites, consulting experts and organizing teleconferences. Finally, the article has been reviewed by an independent panel of international experts. RESULTS: Strict governance is indispensable in surveillance networks, especially when manual decisions are replaced by algorithms and electronically stored routine-care data are reused for the purpose of surveillance. For endorsement of AS networks, governance aspects specifically related to AS networks need to be addressed. Key considerations include enabling participation and inclusion, trust in the collection, use and quality of data (including data protection), accountability and transparency. CONCLUSIONS: This article on governance aspects can be used by coordinating centres and healthcare facilities participating in an AS network as a starting point to set up governance structures. Involvement of main stakeholders and legal specialists early in the development of an AS network is important for endorsement, inclusivity and compliance with the laws and regulations that apply.

PRAISE: providing a roadmap for automated infection surveillance in Europe.

INTRODUCTION: Healthcare-associated infections (HAI) are among the most common adverse events of medical care. Surveillance of HAI is a key component of successful infection prevention programmes. Conventional surveillance - manual chart review - is resource intensive and limited by concerns regarding interrater reliability. This has led to the development and use of automated surveillance (AS). Many AS systems are the product of in-house development efforts and heterogeneous in their design and methods. With this roadmap, the PRAISE network aims to provide guidance on how to move AS from the research setting to large-scale implementation, and how to ensure the delivery of surveillance data that are uniform and useful for improvement of quality of care. METHODS: The PRAISE network brings together 30 experts from ten European countries. This roadmap is based on the outcome of two workshops, teleconference meetings and review by an independent panel of international experts. RESULTS: This roadmap focuses on the surveillance of HAI within networks of healthcare facilities for the purpose of comparison, prevention and quality improvement initiatives. The roadmap does the following: discusses the selection of surveillance targets, different organizational and methodologic approaches and their advantages, disadvantages and risks; defines key performance requirements of AS systems and suggestions for their design; provides guidance on successful implementation and maintenance; and discusses areas of future research and training requirements for the infection prevention and related disciplines. The roadmap is supported by accompanying documents regarding the governance and information technology aspects of implementing AS. CONCLUSIONS: Large-scale implementation of AS requires guidance and coordination within and across surveillance networks. Transitions to large-scale AS entail redevelopment of surveillance methods and their interpretation, intensive dialogue with stakeholders and the investment of considerable resources. This roadmap can be used to guide future steps towards implementation, including designing solutions for AS and practical guidance checklists.

Validation of an algorithm for semiautomated surveillance to detect deep surgical site infections after primary total hip or knee arthroplasty-A multicenter study.

OBJECTIVE: Surveillance of healthcare-associated infections is often performed by manual chart review. Semiautomated surveillance may substantially reduce workload and subjective data interpretation. We assessed the validity of a previously published algorithm for semiautomated surveillance of deep surgical site infections (SSIs) after total hip arthroplasty (THA) or total knee arthroplasty (TKA) in Dutch hospitals. In addition, we explored the ability of a hospital to automatically select the patients under surveillance. DESIGN: Multicenter retrospective cohort study. METHODS: Hospitals identified patients who underwent THA or TKA either by procedure codes or by conventional surveillance. For these patients, routine care data regarding microbiology results, antibiotics, (re)admissions, and surgeries within 120 days following THA or TKA were extracted from electronic health records. Patient selection was compared with conventional surveillance and patients were retrospectively classified as low or high probability of having developed deep SSI by the algorithm. Sensitivity, positive predictive value (PPV), and workload reduction were calculated and compared to conventional surveillance. RESULTS: Of 9,554 extracted THA and TKA surgeries, 1,175 (12.3%) were revisions, and 8,378 primary surgeries remained for algorithm validation (95 deep SSIs, 1.1%). Sensitivity ranged from 93.6% to 100% and PPV ranged from 55.8% to 72.2%. Workload was reduced by ≥98%. Also, 2 SSIs (2.1%) missed by the algorithm were explained by flaws in data selection. CONCLUSIONS: This algorithm reliably detects patients with a high probability of having developed deep SSI after THA or TKA in Dutch hospitals. Our results provide essential information for successful implementation of semiautomated surveillance for deep SSIs after THA or TKA.

Feasibility of Collecting Multiple Patient-Reported Outcome Measures Alongside the Dutch Arthroplasty Register.

BACKGROUND: Compliance rates with patient-reported outcome measures (PROMs) collected alongside arthroplasty registries vary in the literature. We described the feasibility of a routinely collected set PROMs alongside the Dutch Arthroplasty Register. METHODS: The longitudinal Leiden Orthopaedics Outcomes of OsteoArthritis Study is a multicenter (7 hospitals), observational study including patients undergoing total hip or total knee arthroplasty (THA or TKA). A set of PROMs: Short Form-12, EuroQol 5 Dimensions, Hip/Knee injury and Osteoarthritis Outcome Score, Oxford Hip/Knee Score was collected preoperatively and at 6, 12, 24 months, and every 2 years thereafter. Participation rates and response rates were recorded. RESULTS: Between June 2012 and December 2014, 1796 THA and 1636 TKA patients were invited, of whom 1043 THA (58%; mean age 68 years [standard deviation, SD: 10]) and 970 TKA patients (59%; mean age 71 years [SD 9.5]) participated in the study. At 6 months, 35 THA/38 TKA patients were lost to follow-up. Response rates were 90% for THA (898/1000) and 89% for TKA (827/932) participants. At 1 and 2 years, 8 and 18 THA and 17 and 11 TKA patients were lost to follow-up, respectively. The response rates among those eligible were 87% (866/992) and 84% (812/972) for THA and 84% (771/917) and 83% (756/906) for TKA patients, respectively. The 2-year questionnaire was completed by 78.5% of the included THA patients and by 77.9% of the included TKA patients. CONCLUSIONS: About 60% of patients undergoing THA or TKA complete PROMs preoperatively, with more than 80% returning follow-up PROMs. To increase the participation rates, more efforts concerning the initial recruitment of patients are needed.

A framework to develop semiautomated surveillance of surgical site infections: An international multicenter study.

OBJECTIVE: Automated surveillance of healthcare-associated infections reduces workload and improves standardization, but it has not yet been adopted widely. In this study, we assessed the performance and feasibility of an easy implementable framework to develop algorithms for semiautomated surveillance of deep incisional and organ-space surgical site infections (SSIs) after orthopedic, cardiac, and colon surgeries. DESIGN: Retrospective cohort study in multiple countries. METHODS: European hospitals were recruited and selected based on the availability of manual SSI surveillance data from 2012 onward (reference standard) and on the ability to extract relevant data from electronic health records. A questionnaire on local manual surveillance and clinical practices was administered to participating hospitals, and the information collected was used to pre-emptively design semiautomated surveillance algorithms standardized for multiple hospitals and for center-specific application. Algorithm sensitivity, positive predictive value, and reduction of manual charts requiring review were calculated. Reasons for misclassification were explored using discrepancy analyses. RESULTS: The study included 3 hospitals, in the Netherlands, France, and Spain. Classification algorithms were developed to indicate procedures with a high probability of SSI. Components concerned microbiology, prolonged length of stay or readmission, and reinterventions. Antibiotics and radiology ordering were optional. In total, 4,770 orthopedic procedures, 5,047 cardiac procedures, and 3,906 colon procedures were analyzed. Across hospitals, standardized algorithm sensitivity ranged between 82% and 100% for orthopedic surgery, between 67% and 100% for cardiac surgery, and between 84% and 100% for colon surgery, with 72%-98% workload reduction. Center-specific algorithms had lower sensitivity. CONCLUSIONS: Using this framework, algorithms for semiautomated surveillance of SSI can be successfully developed. The high performance of standardized algorithms holds promise for large-scale standardization.

More than 95% completeness of reported procedures in the population-based Dutch Arthroplasty Register.

BACKGROUND AND PURPOSE: A complete and correct national arthroplasty register is indispensable for the quality of arthroplasty outcome studies. We evaluated the coverage, completeness, and validity of the Dutch Arthroplasty Register (LROI) for hip and knee arthroplasty. PATIENTS AND METHODS: The LROI is a nationwide population-based registry with information on joint arthroplasties in the Netherlands. Completeness of entered procedures was validated in 2 ways: (1) by comparison with the number of reimbursements for arthroplasty surgeries (Vektis database), and (2) by comparison with data from hospital information systems (HISs). The validity was examined by conducting checks on missing or incorrectly coded values in the LROI. RESULTS: The LROI contains over 300,000 hip and knee arthroplasties performed since 2007. Coverage of all Dutch hospitals (n = 100) was reached in 2012. Completeness of registered procedures was 98% for hip arthroplasty and 96% for knee arthroplasty in 2012, based on Vektis data. Based on comparison with data from the HIS, completeness of registered procedures was 97% for primary total hip arthroplasty and 96% for primary knee arthroplasty in 2013. Completeness of revision arthroplasty was 88% for hips and 90% for knees in 2013. The proportion of missing or incorrectly coded values of variables was generally less than 0.5%, except for encrypted personal identity numbers (17% of which were missing) and ASA scores (10% of which were missing). INTERPRETATION: The LROI now contains over 300,000 hip and knee arthroplasty procedures, with coverage of all hospitals. It has a good level of completeness (i.e. more than 95% for primary hip and knee arthroplasty procedures in 2012 and 2013) and the database has high validity.

SCOPA-cognition cutoff value for detection of Parkinson's disease dementia.

The SCOPA-Cognition is a reliable and valid test to evaluate cognitive functioning in Parkinson's disease and is widely used in clinical and research settings. Recently, the Movement Disorder Society introduced criteria for Parkinson's disease dementia. The objective of the present study was to use these criteria to determine SCOPA-Cognition cutoffs for maximum accuracy, screening, and diagnosing of Parkinson's disease dementia. A total of 282 patients with Parkinson's disease were assessed with the SCOPA-Cognition and the Movement Disorder Society's Parkinson's disease dementia criteria. From the 275 patients with a complete assessment of the dementia criteria, 12% (n = 32) fulfilled the criteria. Data from 268 patients with complete assessments of both the dementia criteria and the SCOPA-Cognition were used to determine cutoffs for maximum accuracy, screening, and diagnosing of Parkinson's disease dementia. The area under the curve was 0.91 (95% confidence interval, 0.85-0.97), showing a strong association between the dementia criteria and the SCOPA-Cognition. The cutoff for maximum accuracy was 22/23, based on the highest sum of sensitivity (0.80) and specificity (0.87), with positive and negative predictive values of 0.43 and 0.97, respectively. The optimal screening cutoff was 24/25, and the optimal diagnostic cutoff was 17/18. Using the recently published Parkinson's disease dementia criteria as a reference, the current study presents SCOPA-Cognition cutoffs for maximum accuracy, screening, and diagnosing of Parkinson's disease dementia. The availability of SCOPA-Cognition cutoffs for Parkinson's disease dementia may contribute to the scale's usefulness and promote its further use in both clinical and research settings.

Clinical subtypes of Parkinson's disease.

The clinical heterogeneity of Parkinson's disease (PD) may point at the existence of subtypes. Because subtypes likely reflect distinct underlying etiologies, their identification may facilitate future genetic and pharmacotherapeutic studies. Aim of this study was to identify subtypes by a data-driven approach applied to a broad spectrum of motor and nonmotor features of PD. Data of motor and nonmotor PD symptoms were collected in 802 patients in two different European prevalent cohorts. A model-based cluster analysis was conducted on baseline data of 344 patients of a Dutch cohort (PROPARK). Reproducibility of these results was tested in data of the second annual assessment of the same cohort and validated in an independent Spanish cohort (ELEP) of 357 patients. The subtypes were subsequently characterized on clinical and demographic variables. Four similar PD subtypes were identified in two different populations and are largely characterized by differences in the severity of nondopaminergic features and motor complications: Subtype 1 was mildly affected in all domains, Subtype 2 was predominantly characterized by severe motor complications, Subtype 3 was affected mainly on nondopaminergic domains without prominent motor complications, while Subtype 4 was severely affected on all domains. The subtypes had largely similar mean disease durations (nonsignificant differences between three clusters) but showed considerable differences with respect to their association with demographic and clinical variables. In prevalent disease, PD subtypes are largely characterized by the severity of nondopaminergic features and motor complications and likely reflect complex interactions between disease mechanisms, treatment, aging, and gender.

Prevalence and clinical profile of restless legs syndrome in Parkinson's disease.

Parkinson's disease (PD) and restless legs syndrome (RLS) have a dopaminergic link. More insight in the clinical profile of RLS in patients with PD may benefit our understanding of this link. The aims of this study were to evaluate the frequency and clinical profile of RLS in a large cohort of PD patients. In 269 nondemented Caucasian PD patients, the four diagnostic criteria for RLS were administered by a RLS trained researcher. In patients with definite RLS, the severity of these symptoms was assessed. Furthermore, in all patients, relevant motor and nonmotor symptoms in PD were evaluated. Definite RLS was present in 11% of the patients. RLS patients were more often female (69% vs. 32%, P < 0.001), but no other significant differences existed between PD patients with and without RLS. Within the PD patients with RLS, severity of RLS correlated positively with PD severity, motor fluctuations, depressive symptoms, daytime sleepiness, cognitive problems, autonomic symptoms, and psychotic symptoms. This study in a large PD cohort shows that prevalence of RLS is similar to that in the general population, which might be caused by underestimation of RLS due to dopaminergic treatment. No relations were found between the presence of RLS and PD symptoms, but the severity of RLS was related to the severity of PD-related, mainly nondopaminergic, symptoms. It is hypothesized that, nondopaminergic systems, such as the noradrenergic system may play a role in the possible link between PD and RLS.

The identification of Parkinson's disease subtypes using cluster analysis: a systematic review.

The clinical variability between patients with Parkinson's disease (PD) may point at the existence of subtypes of the disease. Identification of subtypes is important, since a focus on homogeneous groups may enhance the chance of success of research on mechanisms of disease and may also lead to tailored treatment strategies. Cluster analysis (CA) is an objective method to classify patients into subtypes. We systematically reviewed the methodology and results of CA studies in PD to gain a better understanding of the robustness of identified subtypes. We found seven studies that fulfilled the inclusion criteria. Studies were limited by incomplete reporting and methodological limitations. Differences between studies rendered comparisons of the results difficult. However, it appeared that studies which applied a comparable design identified similar subtypes. The cluster profiles "old age-at-onset and rapid disease progression" and "young age-at-onset and slow disease progression" emerged from the majority of studies. Other cluster profiles were less consistent across studies. Future studies with a rigorous study design that is standardized with respect to the included variables, data processing, and CA technique may advance the knowledge on subtypes in PD.

Motor patterns in Parkinson's disease: a data-driven approach.

To identify patterns of motor disturbances in Parkinson's disease (PD) and evaluate their relation with other PD domains. A cohort of 399 PD patients was randomly divided into two samples. Factors within the motor section of the SPES/SCOPA were identified by exploratory factor analysis on data from the first sample and next tested by confirmatory factor analysis in the second sample. Relations with other PD domains were evaluated by regression analyses. A four factor model was found to be valid. This included a tremor, a bradykinetic-rigid, and two axial factors. One axial factor ("rise", "gait", "postural instability") was associated with age and cognition, while the other axial factor ("freezing", "speech", "swallowing") was related to dopaminergic medication and complications of therapy. Both other factors showed no relevant associations with demographic and clinical characteristics. The identification of motor factors and their relation with other domains of the disease may help to elucidate the mechanisms responsible for these associations and provide an objective base for further research on subtypes in PD.

Psychotic and compulsive symptoms in Parkinson's disease.

The objective of this study is to evaluate psychiatric symptoms in Parkinson's disease (PD) patients and to assess their relation with other clinical aspects of PD. Psychotic symptoms (PS) and compulsive symptoms (CS) as well as other nonmotor and motor features were evaluated in 353 PD patients. Psychotic and compulsive symptom scores did not correlate significantly. PS occurred in 65% of patients, with item frequencies ranging from 10% (paranoid ideation) to 55% (altered dream phenomena). Regression analysis showed that autonomic impairment accounted for 20% of the 32% explained variance of PS, whereas cognitive problems, depression, daytime sleepiness, and dopamine agonist (DA) dose explained the rest. CS occurred in 19%, with item frequencies of 10% for both sexual preoccupation and compulsive shopping/gambling. Patients with more severe CS (score > or = 2 on one or both items) were significantly more often men, had a younger age at onset, a higher DA dose and experienced more motor fluctuations compared to the other patients. PS and CS are common but unrelated psychiatric symptoms in PD. The relations found between PS and cognitive problems, depression, daytime sleepiness, and autonomic impairment suggests a resemblance with Dementia with Lewy Bodies. The prominent association between PS and autonomic impairment may be explained by a shared underlying mechanism. Our results confirm previous reports on the profile of patients developing CS, and mechanisms underlying motor fluctuations may also play a role in the development of CS in PD.

Genotypic and phenotypic characteristics of Dutch patients with early onset Parkinson's disease.

Early onset Parkinson's disease (EOPD) has been associated with mutations in the Parkin, DJ-1, PINK1, LRRK2, and SNCA genes. The aim of this study is to assess the contribution of these genes in a Dutch EOPD cohort and the phenotypic characteristics of the mutation carriers. A total of 187 unrelated Dutch EOPD patients (age at onset < or = 50 years) were phenotyped and screened for mutations in all exons of Parkin, DJ-1, and PINK1 by direct sequencing and gene dosage analysis. Additionally, analysis of the A30P mutation and exon dosage of SNCA and sequencing of exons 19,31,35,38,41, and 48 of LRRK2 was performed. Pathogenic variations could explain disease in 4% (7 of 187) of the patients including five patients carrying homozygous or compound heterozygous mutations in Parkin, one with a novel homozygous deletion in DJ-1 (P158Del) and one with a heterozygous mutation in LRRK2 (T2356I). We found seven novel mutations. The phenotypic characteristics of mutation carriers varied widely, comparable to the variability seen in sporadic EOPD. Parkin is the most frequently mutated gene in this EOPD cohort, followed by DJ-1, PINK1 and LRRK2. The low overall mutation frequency indicates that the extrapolation of mutation frequencies from other populations should be applied with caution.

A comparative study of odor identification and odor discrimination deficits in Parkinson's disease.

The aim of this study was to compare the characteristics of odor discrimination and odor identification deficits in a large population of patients with Parkinson's disease (PD) and to determine which of these olfactory tests best distinguishes between patients with PD and control subjects. Olfactory performance was assessed in 404 patients with PD and 150 controls, using the odor identification and discrimination parts of the Sniffin' Sticks battery. Mean identification and discrimination scores in patients with PD were significantly lower than in controls. Linear regression analysis using a 95% confidence interval revealed that, relative to the performance of controls, 65.0% of patients with PD had an impairment in odor identification, whereas 42.1% of patients were impaired on the odor discrimination task. ROC curves revealed a higher sensitivity and specificity for odor identification than for odor discrimination in separating patients from controls. In patients with PD, odor discrimination performance decreased with increasing disease duration, whereas odor identification was not correlated with disease stage or duration. In PD, odor identification is more frequently impaired than odor discrimination and allows a better discrimination between patients and controls. Although an odor identification deficit is generally believed to be independent of disease progression, the impairment in odor discrimination appears to increase with disease duration.

Nighttime sleep problems and daytime sleepiness in Parkinson's disease.

Our objective is to evaluate nighttime sleep problems (NSP) and daytime sleepiness (DS) in patients with Parkinson's disease (PD) compared to controls, and to assess relations with demographic, disease-related, and clinical characteristics in patients. NSP and DS were evaluated with the SCOPA-SLEEP questionnaire in PD patients and controls. In patients, other disease-related and clinical characteristics were also evaluated. Four hundred twenty PD patients [mean (SD) age 61.1 (11.5) years] and 150 controls [mean (SD) age 60.9 (9.9) years] participated in the study. Compared to controls, a significantly greater proportion of patients had excessive DS (EDS) (43 vs. 10%), excessive NSP (ENSP) (27 vs. 9%), or used sleep medication (17 vs. 12%). Difficulties with falling asleep were similar in both groups. In both patients and controls, women experienced more NSP than men. In patients, depressive symptoms accounted for 21% of NSP variance and was the major contributor to the total explained variance (30%). Furthermore, NSP were related to dopamine-agonist and levodopa dose, whereas DS was related to age, dopamine-agonist dose, and disease severity. NSP and DS occur frequently in PD, with EDS being reported more commonly than ENSP. No strong relations were found between DS and demographic or clinical variables. The strong relation between NSP and depressive symptoms in PD calls for future studies to explore the nature of this relation.