Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds

In automatic milking systems (AMSs), the detection of clinical mastitis (CM) and the subsequent separation of abnormal milk should be reliably performed by commercial AMSs. Therefore, the objectives of this cross-sectional study were (1) to determine the sensitivity (SN) and specificity (SP) of CM detection of AMS by the four most common manufacturers in Bavarian dairy farms, and (2) to identify routinely collected cow data (AMS and monthly test day data of the regional Dairy Herd Improvement Association (DHIA)) that could improve the SN and SP of clinical mastitis detection. Bavarian dairy farms with AMS from the manufacturers DeLaval, GEA Farm Technologies, Lely, and Lemmer-Fullwood were recruited with the aim of sampling at least 40 cows with clinical mastitis per AMS manufacturer in addition to clinically healthy ones. During a single farm visit, cow-level milking information was first electronically extracted from each AMS and then all lactating cows examined for their udder health status in the barn. Clinical mastitis was defined as at least the presence of visibly abnormal milk. In addition, available DHIA test results from the previous six months were collected. None of the manufacturers provided a definition for clinical mastitis (i.e., visually abnormal milk), therefore, the SN and SP of AMS warning lists for udder health were assessed for each manufacturer individually, based on the clinical evaluation results. Generalized linear mixed models (GLMMs) with herd as random effect were used to determine the potential influence of routinely recorded parameters on SN and SP. A total of 7411 cows on 114 farms were assessed; of these, 7096 cows could be matched to AMS data and were included in the analysis. The prevalence of clinical mastitis was 3.4% (239 cows). When considering the 95% confidence interval (95% CI), all but one manufacturer achieved the minimum SN limit of >80%: DeLaval (SN: 61.4% (95% CI: 49.0%–72.8%)), GEA (75.9% (62.4%–86.5%)), Lely (78.2% (67.4%–86.8%)), and Lemmer-Fullwood (67.6% (50.2%–82.0%)). However, none of the evaluated AMSs achieved the minimum SP limit of 99%: DeLaval (SP: 89.3% (95% CI: 87.7%–90.7%)), GEA (79.2% (77.1%–81.2%)), Lely (86.2% (84.6%–87.7%)), and Lemmer-Fullwood (92.2% (90.8%–93.5%)). All AMS manufacturers’ robots showed an association of SP with cow classification based on somatic cell count (SCC) measurement from the last two DHIA test results: cows that were above the threshold of 100,000 cells/mL for subclinical mastitis on both test days had lower chances of being classified as healthy by the AMS compared to cows that were below the threshold. In conclusion, the detection of clinical mastitis cases was satisfactory across AMS manufacturers. However, the low SP will lead to unnecessarily discarded milk and increased workload to assess potentially false-positive mastitis cases. Based on the results of our study, farmers must evaluate all available data (test day data, AMS data, and daily assessment of their cows in the barn) to make decisions about individual cows and to ultimately ensure animal welfare, food quality, and the economic viability of their farm

Symptoms and quality of life in late stage Parkinson syndromes: a longitudinal community study of predictive factors

BACKGROUND Palliative care is increasingly offered earlier in the cancer trajectory but rarely in Idiopathic Parkinson's Disease(IPD), Progressive Supranuclear Palsy(PSP) or Multiple System Atrophy(MSA). There is little longitudinal data of people with late stage disease to understand levels of need. We aimed to determine how symptoms and quality of life of these patients change over time; and what demographic and clinical factors predicted changes. METHODS We recruited 82 patients into a longitudinal study, consenting patients with a diagnosis of IPD, MSA or PSP, stages 3-5 Hoehn and Yahr(H&Y). At baseline and then on up to 3 occasions over one year, we collected self-reported demographic, clinical, symptom, palliative and quality of life data, using Parkinson's specific and generic validated scales, including the Palliative care Outcome Scale (POS). We tested for predictors using multivariable analysis, adjusting for confounders. FINDINGS Over two thirds of patients had severe disability, over one third being wheelchair-bound/bedridden. Symptoms were highly prevalent in all conditions - mean (SD) of 10.6(4.0) symptoms. More than 50% of the MSA and PSP patients died over the year. Over the year, half of the patients showed either an upward (worsening, 24/60) or fluctuant (8/60) trajectory for POS and symptoms. The strongest predictors of higher levels of symptoms at the end of follow-up were initial scores on POS (AOR 1.30; 95%CI:1.05-1.60) and being male (AOR 5.18; 95% CI 1.17 to 22.92), both were more predictive than initial H&Y scores. INTERPRETATION The findings point to profound and complex mix of non-motor and motor symptoms in patients with late stage IPD, MSA and PSP. Symptoms are not resolved and half of the patients deteriorate. Palliative problems are predictive of future symptoms, suggesting that an early palliative assessment might help screen for those in need of earlier intervention

Functionality and feedback: a protocol for a realist synthesis of the collation, interpretation and utilisation of PROMs data to improve patient care.

Introduction: The feedback and public reporting of PROMs data aims to improve the quality of care provided to patients. Existing systematic reviews have found it difficult to draw overall conclusions about the effectiveness of PROMs feedback. We aim to execute a realist synthesis of the evidence to understand by what means and in what circumstances the feedback of PROMs data leads to the intended service improvements. Methods and analysis: Realist synthesis involves (stage 1) identifying the ideas, assumptions or ‘programme theories’ which explain how PROMs feedback is supposed to work and in what circumstances and then (stage 2) reviewing the evidence to determine the extent to which these expectations are met in practice. For stage 1, six provisional ‘functions’ of PROMs feedback have been identified to structure our review (screening, monitoring, patient involvement, demand management, quality improvement and patient choice). For each function, we will identify the different programme theories that underlie these different goals and develop a logical map of the respective implementation processes. In stage 2, we will identify studies that will provide empirical tests of each component of the programme theories to evaluate the circumstances in which the potential obstacles can be overcome and whether and how the unintended consequences of PROMs feedback arise. We will synthesise this evidence to (1) identify the implementation processes which support or constrain the successful collation, interpretation and utilisation of PROMs data; (2) identify the implementation processes through which the unintended consequences of PROMs data arise and those where they can be avoided. Ethics and dissemination: The study will not require NHS ethics approval. We have secured ethical approval for the study from the University of Leeds (LTSSP-019). We will disseminate the findings of the review through a briefing paper and dissemination event for National Health Service stakeholders, conferences and peer reviewed publications

Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds

In automatic milking systems (AMSs), the detection of clinical mastitis (CM) and the subsequent separation of abnormal milk should be reliably performed by commercial AMSs. Therefore, the objectives of this cross-sectional study were (1) to determine the sensitivity (SN) and specificity (SP) of CM detection of AMS by the four most common manufacturers in Bavarian dairy farms, and (2) to identify routinely collected cow data (AMS and monthly test day data of the regional Dairy Herd Improvement Association (DHIA)) that could improve the SN and SP of clinical mastitis detection. Bavarian dairy farms with AMS from the manufacturers DeLaval, GEA Farm Technologies, Lely, and Lemmer-Fullwood were recruited with the aim of sampling at least 40 cows with clinical mastitis per AMS manufacturer in addition to clinically healthy ones. During a single farm visit, cow-level milking information was first electronically extracted from each AMS and then all lactating cows examined for their udder health status in the barn. Clinical mastitis was defined as at least the presence of visibly abnormal milk. In addition, available DHIA test results from the previous six months were collected. None of the manufacturers provided a definition for clinical mastitis (i.e., visually abnormal milk), therefore, the SN and SP of AMS warning lists for udder health were assessed for each manufacturer individually, based on the clinical evaluation results. Generalized linear mixed models (GLMMs) with herd as random effect were used to determine the potential influence of routinely recorded parameters on SN and SP. A total of 7411 cows on 114 farms were assessed; of these, 7096 cows could be matched to AMS data and were included in the analysis. The prevalence of clinical mastitis was 3.4% (239 cows). When considering the 95% confidence interval (95% CI), all but one manufacturer achieved the minimum SN limit of >80%: DeLaval (SN: 61.4% (95% CI: 49.0%–72.8%)), GEA (75.9% (62.4%–86.5%)), Lely (78.2% (67.4%–86.8%)), and Lemmer-Fullwood (67.6% (50.2%–82.0%)). However, none of the evaluated AMSs achieved the minimum SP limit of 99%: DeLaval (SP: 89.3% (95% CI: 87.7%–90.7%)), GEA (79.2% (77.1%–81.2%)), Lely (86.2% (84.6%–87.7%)), and Lemmer-Fullwood (92.2% (90.8%–93.5%)). All AMS manufacturers’ robots showed an association of SP with cow classification based on somatic cell count (SCC) measurement from the last two DHIA test results: cows that were above the threshold of 100,000 cells/mL for subclinical mastitis on both test days had lower chances of being classified as healthy by the AMS compared to cows that were below the threshold. In conclusion, the detection of clinical mastitis cases was satisfactory across AMS manufacturers. However, the low SP will lead to unnecessarily discarded milk and increased workload to assess potentially false-positive mastitis cases. Based on the results of our study, farmers must evaluate all available data (test day data, AMS data, and daily assessment of their cows in the barn) to make decisions about individual cows and to ultimately ensure animal welfare, food quality, and the economic viability of their farm