Trends in low vision service utilisation: A retrospective study based on general population healthcare claims.

PURPOSE: To identify parameters associated with the downward trend in the uptake of Low Vision Services (LVS) in the Netherlands. METHODS: A retrospective cohort study was conducted based on a Dutch national health insurance claims database (Vektis CV) of all adults (≥18 years) who received LVS from 2015 until 2018. Descriptive statistics were used to assess socio-demographic, clinical and contextual characteristics and other healthcare utilisation of the study population. General estimating equations trends in characteristics and healthcare utilisation were determined over time. RESULTS: A total of 49,726 unique patients received LVS, but between 2015 and 2018, the number of patients decreased by 15%. The majority was aged 65 years or older (53%), female (54%), had a middle (38%) or low (24%) socio-economic status and lived in urban areas (68%). Between 2015-2018, significant downward trends were found for treatment with intravitreal injections and lens-related diseases for LVS patients. For physical comorbidity, utilisation of ophthalmic care, low vision aids and occupational therapy, a significant upward trend was found over time. CONCLUSION: The decrease of Dutch LVS patients by 15% between 2015 and 2018 might be explained by a reduced distribution of patients treated with intravitreal injections and patients with lens-related diseases within the LVS. Compared to 2015, patients were more likely to have physical comorbidity, to see an ophthalmologist and to use low vision aids and occupational therapy in 2016, 2017 and 2018. This might indicate enhanced access to LVS when treated by ophthalmologists or within other medical specialties, or the opposite, i.e., less access when not treated within one of these medical specialties. Future research is needed to examine differences in patterns between LVS users and non-users further.

The association between outcome-based quality indicators for intensive care units.

PURPOSE: To assess and improve the effectiveness of ICU care, in-hospital mortality rates are often used as principal quality indicator for benchmarking purposes. Two other often used, easily quantifiable, quality indicators to assess the efficiency of ICU care are based on readmission to the ICU and ICU length of stay. Our aim was to examine whether there is an association between case-mix adjusted outcome-based quality indicators in the general ICU population as well as within specific subgroups. MATERIALS AND METHODS: We included patients admitted in 2015 of all Dutch ICUs. We derived the standardized in-hospital mortality ratio (SMR); the standardized readmission ratio (SRR); and the standardized length of stay ratio (SLOSR). We expressed association through Pearson's correlation coefficients. RESULTS: The SMR ranged from 0.6 to 1.5; the SRR ranged from 0.7 to 2.1; and the SLOSR ranged from 0.7 to 1.3. For the total ICU population we found no significant associations. We found a positive, non-significant, association between SMR and SLOSR for admissions with low-mortality risk, (r = 0.25; p = 0.024), and a negative association between these indicators for admissions with high-mortality risk (r = -0.49; p<0.001). CONCLUSION: Overall, we found no association at ICU population level. Differential associations were found between performance on mortality and length of stay within different risk strata. We recommend users of quality information to take these three outcome indicators into account when benchmarking ICUs as they capture different aspects of ICU performance. Furthermore, we suggest to report quality indicators for patient subgroups.

Is patient length of stay associated with intensive care unit characteristics?

PURPOSE: We described the association between Intensive care units (ICU) characteristics and ICU Length of stay (LoS), after correcting for patient characteristics. We also compared the predictive performances of models including either patient and ICU characteristics or only patient characteristics. MATERIALS AND METHODS: We included all admissions of 38 ICUs participating in the Dutch National Intensive Care Evaluation registry (NICE) between 2014 and 2016. We performed mixed effect regression including, one ICU characteristic in each model and a random intercept per ICU. Furthermore, we developed a prediction model containing multiple ICU characteristics and patients characteristics. RESULTS: We found negative associations for the number of hospital beds; number of ICU beds; availability of fellows in training for intensivist; full-time equivalent ICU nurses; and discharged in a shift with 100% bed occupancy. Furthermore, we found a U-shaped association with the nurses to patient ratio as spline function. The performance based on R2 was between 0.30 and 0.32 for both the model containing only patient characteristics and the model also containing ICU characteristics. CONCLUSION: After correcting for patient characteristics, we found statistically significant associations between ICU LoS and six ICU characteristics, mainly describing staff availability. Furthermore, we conclude that including ICU characteristics did not significantly improve ICU LoS prediction.

Individual and Clustered Rankability of ICUs According to Case-Mix-Adjusted Mortality.

OBJECTIVES: The performance of ICUs can be compared by ranking them into a league table according to their risk-adjusted mortality rate. The statistical quality of a league table can be expressed as its rankability, the percentage of variation between ICUs attributable to unexplained differences. We examine whether we can improve the rankability of our league table by using data from a longer period or by grouping ICUs with similar performance constructing a league table of clusters rather than individual ICUs. DESIGN: We developed a league table for risk-adjusted mortality rate with its rankability. The effect of assessment period was determined using a resampling procedure. Hierarchical clustering was used to obtain clusters of similar ICUs. PATIENTS: We used data from ICUs participating in the Dutch National Intensive Care Evaluation registry between 2011 and 2013. MEASUREMENTS AND MAIN RESULTS: We constructed league tables using 157,394 admissions from 78 ICUs with risk-adjusted mortality rate between 5.9% and 13.9% per ICU over the inclusion period. The rankability was 73% for 2013 and 89% for the whole period 2011-2013. Rankability over the year 2013 increased till 98% when clustering ICUs, reaching an optimum at a league table of seven clusters. CONCLUSIONS: We conclude that, when using data from a single year, the rankability of a league table of Dutch ICUs based on risk-adjusted mortality rate was unacceptably low. We could improve the rankability of this league table by increasing the period of data collection or by grouping similar ICUs into clusters and constructing a league table of clusters of ICUs rather than individual ICUs. Ranking clusters of ICUs could be useful for identifying possible differences in performance between clusters of ICUs.

Intensive care admission of cancer patients: a comparative analysis.

The aim of this study was to obtain insight into which proportion of cancer patients is admitted to an Intensive Care Unit (ICU) and how their survival, demographic, and clinical characteristics relate to cancer patients not admitted to the ICU. Data from patients registered with cancer between 2006 and 2011 in four hospitals in the Netherlands were linked to the Dutch National Intensive Care Evaluation registry. About 36,860 patients with cancer were identified, of whom 2,374 (6.4%) were admitted to the ICU. Fifty-six percent of ICU admissions were after surgery, whereas 44% were for medical reasons. The risk for ICU admission was highest among cancer patients treated with surgery either alone or combined with chemotherapy and/or radiation therapy. Only 80 of 1,073 medical ICU admissions (3.3%) were for cancer-specific reasons. Although more women (54.0%) than men were registered with cancer, the proportion of male cancer patients admitted to an ICU was much higher (9.3 vs. 4.0%, P < 0.001). Five-year survival of cancer patients admitted to the ICU was substantial (41%) although median survival was much lower (1,104 days) than in patients not admitted to the ICU (median survival time not reached, P < 0.001). These results show that one out of 16 cancer patients was admitted to an ICU and that ICU support for this group should not be considered futile.

Comparison of regression methods for modeling intensive care length of stay.

Intensive care units (ICUs) are increasingly interested in assessing and improving their performance. ICU Length of Stay (LoS) could be seen as an indicator for efficiency of care. However, little consensus exists on which prognostic method should be used to adjust ICU LoS for case-mix factors. This study compared the performance of different regression models when predicting ICU LoS. We included data from 32,667 unplanned ICU admissions to ICUs participating in the Dutch National Intensive Care Evaluation (NICE) in the year 2011. We predicted ICU LoS using eight regression models: ordinary least squares regression on untransformed ICU LoS,LoS truncated at 30 days and log-transformed LoS; a generalized linear model with a Gaussian distribution and a logarithmic link function; Poisson regression; negative binomial regression; Gamma regression with a logarithmic link function; and the original and recalibrated APACHE IV model, for all patients together and for survivors and non-survivors separately. We assessed the predictive performance of the models using bootstrapping and the squared Pearson correlation coefficient (R2), root mean squared prediction error (RMSPE), mean absolute prediction error (MAPE) and bias. The distribution of ICU LoS was skewed to the right with a median of 1.7 days (interquartile range 0.8 to 4.0) and a mean of 4.2 days (standard deviation 7.9). The predictive performance of the models was between 0.09 and 0.20 for R2, between 7.28 and 8.74 days for RMSPE, between 3.00 and 4.42 days for MAPE and between -2.99 and 1.64 days for bias. The predictive performance was slightly better for survivors than for non-survivors. We were disappointed in the predictive performance of the regression models and conclude that it is difficult to predict LoS of unplanned ICU admissions using patient characteristics at admission time only.

Trends in admission prevalence, illness severity and survival of haematological patients treated in Dutch intensive care units.

PURPOSE: To explore trends over time in admission prevalence and (risk-adjusted) mortality of critically ill haematological patients and compare these trends to those of several subgroups of patients admitted to the medical intensive care unit (medical ICU patients). METHODS: A total of 1,741 haematological and 60,954 non-haematological patients admitted to the medical ICU were analysed. Trends over time and differences between two subgroups of haematological medical ICU patients and four subgroups of non-haematological medical ICU patients were assessed, as well as the influence of leukocytopenia. RESULTS: The proportion of haematological patients among all medical ICU patients increased over time [odds ratio (OR) 1.06; 95 % confidence interval (CI) 1.03-1.10 per year; p < 0.001]. Risk-adjusted mortality was significantly higher for haematological patients admitted to the ICU with white blood cell (WBC) counts of <1.0 × 10(9)/L (47 %; 95 % CI 41-54 %) and ≥1.0 × 10(9)/L (45 %; 95 % CI 42-49 %), respectively, than for patients admitted with chronic heart failure (27 %; 95 % CI 26-28 %) and with chronic liver cirrhosis (38 %; 95 % CI 35-42 %), but was not significantly different from patients admitted with solid tumours (40 %; 95 % CI 36-45 %). Over the years, the risk-adjusted hospital mortality rate significantly decreased in both the haematological and non-haematological group with an OR of 0.93 (95 % CI 0.92-0.95) per year. After correction for case-mix using the APACHE-II score (with WBC omitted), a WBC <1.0 × 10(9)/L was not a predictor of mortality in haematological patients (OR 0.86; 95 % CI 0.46-1.64; p = 0.65). We found no case-volume effect on mortality for haematological ICU patients. CONCLUSIONS: An increasing number of haematological patients are being admitted to Dutch ICUs. While mortality is significantly higher in this group of medical ICU patients than in subgroups of non-haematological ones, the former show a similar decrease in raw and risk-adjusted mortality rate over time, while leukocytopenia is not a predictor of mortality. These results suggest that haematological ICU patients have benefitted from improved intensive care support during the last decade.