Causal inference using observational intensive care unit data: a scoping review and recommendations for future practice

This scoping review focuses on the essential role of models for causal inference in shaping actionable artificial intelligence (AI) designed to aid clinicians in decision-making. The objective was to identify and evaluate the reporting quality of studies introducing models for causal inference in intensive care units (ICUs), and to provide recommendations to improve the future landscape of research practices in this domain. To achieve this, we searched various databases including Embase, MEDLINE ALL, Web of Science Core Collection, Google Scholar, medRxiv, bioRxiv, arXiv, and the ACM Digital Library. Studies involving models for causal inference addressing time-varying treatments in the adult ICU were reviewed. Data extraction encompassed the study settings and methodologies applied. Furthermore, we assessed reporting quality of target trial components (i.e., eligibility criteria, treatment strategies, follow-up period, outcome, and analysis plan) and main causal assumptions (i.e., conditional exchangeability, positivity, and consistency). Among the 2184 titles screened, 79 studies met the inclusion criteria. The methodologies used were G methods (61%) and reinforcement learning methods (39%). Studies considered both static (51%) and dynamic treatment regimes (49%). Only 30 (38%) of the studies reported all five target trial components, and only seven (9%) studies mentioned all three causal assumptions. To achieve actionable AI in the ICU, we advocate careful consideration of the causal question of interest, describing this research question as a target trial emulation, usage of appropriate causal inference methods, and acknowledgement (and examination of potential violations of) the causal assumptions.

The effect of cardiovascular risk on disease progression in de novo Parkinson's disease patients: An observational analysis

Background

Currently available treatment options for Parkinson's disease are symptomatic and do not alter the course of the disease. Recent studies have raised the possibility that cardiovascular risk management may slow the progression of the disease.

Objectives

We estimated the effect of baseline cardiovascular risk factors on the progression of Parkinson's disease, using measures for PD-specific motor signs and cognitive functions.

Methods

We used data from 424 de novo Parkinson's disease patients and 199 age-matched controls from the observational, multicenter Parkinson's Progression Markers Initiative (PPMI) study, which included follow-up of up to 9 years. The primary outcome was the severity of PD-specific motor signs, assessed with the MDS-UPDRS part III in the "OFF"-state. The secondary outcome was cognitive function, measured with the Montreal Cognitive Assessment, Symbol Digit Modalities Test, and Letter-Number Sequencing task. Exposures of interest were diabetes mellitus, hypertension, body mass index, cardiovascular event history and hypercholesterolemia, and a modified Framingham risk score, measured at baseline. The effect of each of these exposures on disease progression was modeled using linear mixed models, including adjustment for identified confounders. A secondary analysis on the Tracking Parkinson's cohort including 1,841 patients was performed to validate our findings in an independent patient cohort.

Results

Mean age was 61.4 years, and the average follow-up was 5.5 years. We found no statistically significant effect of any individual cardiovascular risk factor on the MDS-UPDRS part III progression (all 95% confidence intervals (CIs) included zero), with one exception: in the PD group, the estimated effect of a one-point increase in body mass index was 0.059 points on the MDS-UPDRS part III per year (95% CI: 0.017 to 0.102). We found no evidence for an effect of any of the exposures on the rate of change in cognitive functioning in the PD group. Similar results were observed for the Tracking Parkinson's cohort (all 95% CIs overlapped with PPMI), but the 95% CI of the effect of body mass index on the MDS-UPDRS part III progression included zero.

Conclusions

Based on this analysis of two large cohorts of de novo PD patients, we found no evidence to support clinically relevant effects of cardiovascular risk factors on the clinical progression of Parkinson's disease.

The association of comorbidity with Parkinson's disease-related hospitalizations

INTRODUCTION

Unplanned hospital admissions associated with Parkinson's disease could be partly attributable to comorbidities.

METHODS

We studied nationwide claims databases and registries. Persons with newly diagnosed Parkinson's disease were identified based on the first Parkinson's disease-related reimbursement claim by a medical specialist. Comorbidities were classified based on the Charlson Comorbidity Index. We studied hospitalization admissions because of falls, psychiatric diseases, pneumonia and urinary tract infections, PD-related hospitalizations-not otherwise specified. The association between comorbidities and time-to-hospitalization was estimated using Cox proportional hazard modelling. To better understand pathways leading to hospitalizations, we performed multiple analyses on causes for hospitalizations.

RESULTS

We identified 18 586 people with newly diagnosed Parkinson's disease. The hazard of hospitalization was increased in persons with peptic ulcer disease (HR 2.20, p = 0.009), chronic obstructive pulmonary disease (HR 1.61, p < 0.001), stroke (HR 1.37, p = 0.002) and peripheral vascular disease (HR 1.31, p = 0.02). In the secondary analyses, the hazard of PD-related hospitalizations-not otherwise specified (HR 3.24, p = 0.02) and pneumonia-related hospitalization (HR 2.90, p = 0.03) was increased for those with comorbid peptic ulcer disease. The hazard of fall-related hospitalization (HR 1.57, p = 0.003) and pneumonia-related hospitalization (HR 2.91, p < 0.001) was increased in persons with chronic obstructive pulmonary disease. The hazard of pneumonia-related hospitalization was increased in those with stroke (HR 1.54, p = 0.03) or peripheral vascular disease (HR 1.60, p = 0.02). The population attributable risk of comorbidity was 8.4%.

CONCLUSION

Several comorbidities increase the risk of Parkinson's disease related-hospitalization indicating a need for intervention strategies targeting these comorbid disorders.

Development and validation of an early warning model for hospitalized COVID-19 patients: a multi-center retrospective cohort study

Background

Timely identification of deteriorating COVID-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern that widely used Early warning scores (EWSs) underestimate illness severity in COVID-19 patients and therefore, we developed an early warning model specifically for COVID-19 patients.

Methods

We retrospectively collected electronic medical record data to extract predictors and used these to fit a random forest model. To simulate the situation in which the model would have been developed after the first and implemented during the second COVID-19 ‘wave’ in the Netherlands, we performed a temporal validation by splitting all included patients into groups admitted before and after August 1, 2020. Furthermore, we propose a method for dynamic model updating to retain model performance over time. We evaluated model discrimination and calibration, performed a decision curve analysis, and quantified the importance of predictors using SHapley Additive exPlanations values.

Results

We included 3514 COVID-19 patient admissions from six Dutch hospitals between February 2020 and May 2021, and included a total of 18 predictors for model fitting. The model showed a higher discriminative performance in terms of partial area under the receiver operating characteristic curve (0.82 [0.80–0.84]) compared to the National early warning score (0.72 [0.69–0.74]) and the Modified early warning score (0.67 [0.65–0.69]), a greater net benefit over a range of clinically relevant model thresholds, and relatively good calibration (intercept = 0.03 [− 0.09 to 0.14], slope = 0.79 [0.73–0.86]).

Conclusions

This study shows the potential benefit of moving from early warning models for the general inpatient population to models for specific patient groups. Further (independent) validation of the model is needed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40635-022-00465-4.

Density of Patient-Sharing Networks: Impact on the Value of Parkinson Care

BACKGROUND

Optimal care for Parkinson's disease (PD) requires coordination and collaboration between providers within a complex care network. Individual patients have personalised networks of their own providers, creating a unique informal network of providers who treat ('share') the same patient. These 'patient-sharing networks' differ in density, ie, the number of identical patients they share. Denser patient-sharing networks might reflect better care provision, since providers who share many patients might have made efforts to improve their mutual care delivery. We evaluated whether the density of these patient-sharing networks affects patient outcomes and costs.

METHODS

We analysed medical claims data from all PD patients in the Netherlands between 2012 and 2016. We focused on seven professional disciplines that are commonly involved in Parkinson care. We calculated for each patient the density score: the average number of patients that each patient's providers shared. Density scores could range from 1.00 (which might reflect poor collaboration) to 83.00 (which might reflect better collaboration). This score was also calculated at the hospital level by averaging the scores for all patients belonging to a specific hospital. Using logistic and linear regression analyses we estimated the relationship between density scores and health outcomes, healthcare utilization, and healthcare costs.

RESULTS

The average density score varied considerably (average 6.7, SD 8.2). Adjusted for confounders, higher density scores were associated with a lower risk of PD-related complications (odds ratio [OR]: 0.901; P<.001) and with lower healthcare costs (coefficients: -0.018, P=.005). Higher density scores were associated with more frequent involvement of neurologists (coefficient 0.068), physiotherapists (coefficient 0.052) and occupational therapists (coefficient 0.048) (P values all <.001).

CONCLUSION

Patient sharing networks showed large variations in density, which appears unwanted as denser networks are associated with better outcomes and lower costs.

Effect of Genetic Variation in CYP450 on Gonadal Impairment in a European Cohort of Female Childhood Cancer Survivors, Based on a Candidate Gene Approach: Results from the PanCareLIFE Study

BACKGROUND

Female childhood cancer survivors (CCSs) carry a risk of therapy-related gonadal dysfunction. Alkylating agents (AA) are well-established risk factors, yet inter-individual variability in ovarian function is observed. Polymorphisms in CYP450 enzymes may explain this variability in AA-induced ovarian damage. We aimed to evaluate associations between previously identified genetic polymorphisms in CYP450 enzymes and AA-related ovarian function among adult CCSs.

METHODS

Anti-Müllerian hormone (AMH) levels served as a proxy for ovarian function in a discovery cohort of adult female CCSs, from the pan-European PanCareLIFE cohort (n = 743; age (years): median 25.8, interquartile range (IQR) 22.1-30.6). Using two additive genetic models in linear and logistic regression, nine genetic variants in three CYP450 enzymes were analyzed in relation to cyclophosphamide equivalent dose (CED) score and their impact on AMH levels. The main model evaluated the effect of the variant on AMH and the interaction model evaluated the modifying effect of the variant on the impact of CED score on log-transformed AMH levels. Results were validated, and meta-analysis performed, using the USA-based St. Jude Lifetime Cohort (n = 391; age (years): median 31.3, IQR 26.6-37.4).

RESULTS

CYP3A4*3 was significantly associated with AMH levels in the discovery and replication cohort. Meta-analysis revealed a significant main deleterious effect (Beta (95% CI): -0.706 (-1.11--0.298), p-value = 7 × 10-4) of CYP3A4*3 (rs4986910) on log-transformed AMH levels. CYP2B6*2 (rs8192709) showed a significant protective interaction effect (Beta (95% CI): 0.527 (0.126-0.928), p-value = 0.01) on log-transformed AMH levels in CCSs receiving more than 8000 mg/m2 CED.

CONCLUSIONS

Female CCSs CYP3A4*3 carriers had significantly lower AMH levels, and CYP2B6*2 may have a protective effect on AMH levels. Identification of risk-contributing variants may improve individualized counselling regarding the treatment-related risk of infertility and fertility preservation options.

Possible modification of BRSK1 on the risk of alkylating chemotherapy-related reduced ovarian function

STUDY QUESTION

Do genetic variations in the DNA damage response pathway modify the adverse effect of alkylating agents on ovarian function in female childhood cancer survivors (CCS)?

SUMMARY ANSWER

Female CCS carrying a common BR serine/threonine kinase 1 (BRSK1) gene variant appear to be at 2.5-fold increased odds of reduced ovarian function after treatment with high doses of alkylating chemotherapy.

WHAT IS KNOWN ALREADY

Female CCS show large inter-individual variability in the impact of DNA-damaging alkylating chemotherapy, given as treatment of childhood cancer, on adult ovarian function. Genetic variants in DNA repair genes affecting ovarian function might explain this variability.

STUDY DESIGN, SIZE, DURATION

CCS for the discovery cohort were identified from the Dutch Childhood Oncology Group (DCOG) LATER VEVO-study, a multi-centre retrospective cohort study evaluating fertility, ovarian reserve and risk of premature menopause among adult female 5-year survivors of childhood cancer. Female 5-year CCS, diagnosed with cancer and treated with chemotherapy before the age of 25 years, and aged 18 years or older at time of study were enrolled in the current study. Results from the discovery Dutch DCOG-LATER VEVO cohort (n = 285) were validated in the pan-European PanCareLIFE (n = 465) and the USA-based St. Jude Lifetime Cohort (n = 391).

PARTICIPANTS/MATERIALS, SETTING, METHODS

To evaluate ovarian function, anti-Müllerian hormone (AMH) levels were assessed in both the discovery cohort and the replication cohorts. Using additive genetic models in linear and logistic regression, five genetic variants involved in DNA damage response were analysed in relation to cyclophosphamide equivalent dose (CED) score and their impact on ovarian function. Results were then examined using fixed-effect meta-analysis.

MAIN RESULTS AND THE ROLE OF CHANCE

Meta-analysis across the three independent cohorts showed a significant interaction effect (P = 3.0 × 10-4) between rs11668344 of BRSK1 (allele frequency = 0.34) among CCS treated with high-dose alkylating agents (CED score ≥8000 mg/m2), resulting in a 2.5-fold increased odds of a reduced ovarian function (lowest AMH tertile) for CCS carrying one G allele compared to CCS without this allele (odds ratio genotype AA: 2.01 vs AG: 5.00).

LIMITATIONS, REASONS FOR CAUTION

While low AMH levels can also identify poor responders in assisted reproductive technology, it needs to be emphasized that AMH remains a surrogate marker of ovarian function.

WIDER IMPLICATIONS OF THE FINDINGS

Further research, validating our findings and identifying additional risk-contributing genetic variants, may enable individualized counselling regarding treatment-related risks and necessity of fertility preservation procedures in girls with cancer.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by the PanCareLIFE project that has received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no 602030. In addition, the DCOG-LATER VEVO study was funded by the Dutch Cancer Society (Grant no. VU 2006-3622) and by the Children Cancer Free Foundation (Project no. 20) and the St Jude Lifetime cohort study by NCI U01 CA195547. The authors declare no competing interests.

TRIAL REGISTRATION NUMBER

N/A.

Estimating the Effect of Early Treatment Initiation in Parkinson's Disease Using Observational Data

Background

Both patients and physicians may choose to delay initiation of dopamine replacement therapy in Parkinson's disease (PD) for various reasons. We used observational data to estimate the effect of earlier treatment in PD. Observational data offer a valuable source of evidence, complementary to controlled trials.

Method

We studied the Parkinson's Progression Markers Initiative cohort of patients with de novo PD to estimate the effects of duration of PD treatment during the first 2 years of follow‐up, exploiting natural interindividual variation in the time to start first treatment. We estimated the Movement Disorder Society–Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) Part III (primary outcome) and several functionally relevant outcomes at 2, 3, and 4 years after baseline. To adjust for time‐varying confounding, we used marginal structural models with inverse probability of treatment weighting and the parametric g‐formula.

Results

We included 302 patients from the Parkinson's Progression Markers Initiative cohort. There was a small improvement in MDS‐UPDRS Part III scores after 2 years of follow‐up for patients who started treatment earlier, and similar, but nonstatistically significant, differences in subsequent years. We found no statistically significant differences in most secondary outcomes, including the presence of motor fluctuations, nonmotor symptoms, MDS‐UPDRS Part II scores, and the Schwab and England Activities of Daily Living Scale.

Conclusion

Earlier treatment initiation does not lead to worse MDS‐UPDRS motor scores and may offer small improvements. These findings, based on observational data, are in line with earlier findings from clinical trials. Observational data, when combined with appropriate causal methods, are a valuable source of additional evidence to support real‐world clinical decisions. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study

Background

Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments.

Objective

This paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD.

Methods

The Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch’s method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation.

Results

From 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor.

Conclusions

We present a new video-referenced data set that includes unscripted activities in and around the participants’ homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders.

Sex-Specific Patient Journeys in Early Parkinson's Disease in the Netherlands

Objective: To reconstruct a sex-specific patient journey for Dutch persons with Parkinson's disease (PD) during the first 5 years after diagnosis.

Method: We analyzed a national administrative medical claims database containing data of all patients newly diagnosed with PD between 2012 and 2016 in the Netherlands. We performed time-to-event analysis to identify the moments when patients received care from neurologists, allied healthcare therapists or general practitioners. We also extracted relevant clinical milestones: unexpected hospitalization for PD, pneumonia, orthopedic injuries, nursing home admission, and death. Using these data, we constructed the patient journey stratified for sex.

Results: We included claims data of 13,518 men and 8,775 women with newly diagnosed PD in the Netherlands. While we found little difference in neurologist consultations, women visited general practitioners and physiotherapists significantly earlier and more often (all p-values < 0.001). After 5 years, 37.9% (n = 3,326) of women had visited an occupational therapist and 18.5% (n = 1,623) a speech and language therapist at least once. This was 33.1% (n = 4,474) and 23.7% (n = 3,204) for men. Approximately 2 years after diagnosis, PD-related complications (pneumonia, orthopedic injuries, and PD-related hospitalization) occurred for the first time (women: 1.8 years; men: 2.3 years), and after 5 years, 72.9% (n = 6,397) of women, and 68.7% (n = 9,287) of men had experienced at least one.

Discussion: Considering the strengths and limitations of our methods, our findings suggest that women experience complications and access most healthcare services sooner after diagnosis and more frequently than men. The identified sex differences extend the debate about phenotypical differences in PD between men and women.

Measuring Parkinson's disease over time: The real-world within-subject reliability of the MDS-UPDRS

Background

An important challenge in Parkinson's disease research is how to measure disease progression, ideally at the individual patient level. The MDS‐UPDRS, a clinical assessment of motor and nonmotor impairments, is widely used in longitudinal studies. However, its ability to assess within‐subject changes is not well known. The objective of this study was to estimate the reliability of the MDS‐UPDRS when used to measure within‐subject changes in disease progression under real‐world conditions.

Methods

Data were obtained from the Parkinson's Progression Markers Initiative cohort and included repeated MDS‐UPDRS measurements from 423 de novo Parkinson's disease patients (median follow‐up: 54 months). Subtotals were calculated for parts I, II, and III (in on and off states). In addition, factor scores were extracted from each part. A linear Gaussian state space model was used to differentiate variance introduced by long‐lasting changes from variance introduced by measurement error and short‐term fluctuations. Based on this, we determined the within‐subject reliability of 1‐year change scores.

Results

Overall, the within‐subject reliability ranged from 0.13 to 0.62. Of the subscales, parts II and III (OFF) demonstrated the highest within‐subject reliability (both 0.50). Of the factor scores, the scores related to gait/posture (0.62), mobility (0.45), and rest tremor (0.43) showed the most consistent behavior.

Conclusions

Our results highlight that MDS‐UPDRS change scores contain a substantial amount of error variance, underscoring the need for more reliable instruments to forward our understanding of the heterogeneity in PD progression. Focusing on gait and rest tremor may be a promising approach for an early Parkinson's disease population. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.